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It also identifies the transition points in the life cycle where transfers of responsibility occur, and the types of multi-lateral communication that are necessary at these transition points. This document also establishes a coherent concepts and terminology for other standards that address specific aspects of the safety, effectiveness, and security (including privacy) of health software and health IT systems.\u003C\u002Fp>","2021","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F71538.html","2026-01-26T14:35:36.028Z","2026-01-26T23:44:48.924Z","2026-01-26T23:44:49.020Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":72},11,"lko5l1pyyq1670cxx94xc7t3","Published","2026-01-26T14:24:54.097Z","2026-01-28T19:51:05.272Z","2026-01-28T19:51:05.338Z",{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":77,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},163,"bj0rkabzxpb4ul3pgxswf516","i-published.svg",16,{},"i_published_fb60d74250",".svg","image\u002Fsvg+xml",0.56,"\u002Fuploads\u002Fi_published_fb60d74250.svg","2026-01-28T14:44:56.766Z","2026-01-28T19:51:03.609Z","2026-01-28T14:44:56.767Z",{"id":87,"documentId":88,"title":89,"createdAt":90,"updatedAt":90,"publishedAt":91},8,"oh50nj0zxoiti20bl2o9yzwu","Healthcare","2026-01-26T14:26:00.869Z","2026-01-26T14:26:00.897Z",{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},4,"q4p5x9y2yz0igt3glwtcnjp5","ISO","2026-01-26T14:23:13.652Z","2026-01-26T14:23:13.685Z",[99,105,111,117,123,129,135,141,147,153,159],{"id":100,"documentId":101,"title":102,"createdAt":103,"updatedAt":103,"publishedAt":104},33,"wg9rthga2z26veygw0uqpbt9","Health software","2026-01-26T23:39:24.523Z","2026-01-26T23:39:24.693Z",{"id":106,"documentId":107,"title":108,"createdAt":109,"updatedAt":109,"publishedAt":110},34,"cxlcpn6xu1patkjzygcbmq22","Health IT systems","2026-01-26T23:39:24.529Z","2026-01-26T23:39:24.699Z",{"id":112,"documentId":113,"title":114,"createdAt":115,"updatedAt":115,"publishedAt":116},35,"xlsruvmf7ad5dxua0hnbhddi","Safety","2026-01-26T23:39:24.535Z","2026-01-26T23:39:24.705Z",{"id":118,"documentId":119,"title":120,"createdAt":121,"updatedAt":121,"publishedAt":122},36,"lwqcj8jt7eueoasu72gp5ya1","Effectiveness","2026-01-26T23:39:24.540Z","2026-01-26T23:39:24.710Z",{"id":124,"documentId":125,"title":126,"createdAt":127,"updatedAt":127,"publishedAt":128},37,"wg2sxi6s7wwo9uvbu1mlkq1c","Security","2026-01-26T23:39:24.544Z","2026-01-26T23:39:24.714Z",{"id":130,"documentId":131,"title":132,"createdAt":133,"updatedAt":133,"publishedAt":134},41,"b1rgzqx7e9ljygji5np7x032","Life cycle","2026-01-26T23:39:24.585Z","2026-01-26T23:39:24.781Z",{"id":136,"documentId":137,"title":138,"createdAt":139,"updatedAt":139,"publishedAt":140},38,"hm670iy7y37guml5x7r4ru1g","Principles","2026-01-26T23:39:24.550Z","2026-01-26T23:39:24.718Z",{"id":142,"documentId":143,"title":144,"createdAt":145,"updatedAt":145,"publishedAt":146},39,"jpipze3mhjsoozri8czdb7vr","Concepts","2026-01-26T23:39:24.555Z","2026-01-26T23:39:24.723Z",{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},40,"p84hf8ejwzdkkadpxgb6dvak","Terminology","2026-01-26T23:39:24.560Z","2026-01-26T23:39:24.727Z",{"id":154,"documentId":155,"title":156,"createdAt":157,"updatedAt":157,"publishedAt":158},50,"gg1vgwkrbkehdqx9lyvf1h22","Decommissioning","2026-01-26T23:39:24.590Z","2026-01-26T23:39:24.815Z",{"id":160,"documentId":161,"title":162,"createdAt":163,"updatedAt":163,"publishedAt":164},51,"u0c3ikkieelz8svyggb2oq3c","Privacy","2026-01-26T23:39:24.831Z","2026-01-26T23:39:24.923Z",{"id":166,"documentId":167,"number":168,"title":169,"part":11,"abstract":170,"lastUpdatedYear":171,"linkToOfficial":172,"createdAt":173,"updatedAt":173,"publishedAt":174,"featured":11,"image":11,"development_stage":175,"domain":178,"issuing_body":184,"ai_standard_keywords":185},27,"uj2918kkoici79ijr5lmg7eo","ISO 37168:2022","Smart community infrastructures – Guidance on smart transportation by Electric, Connected and Autonomous Vehicles (eCAVs) and its application to on-demand responsive passenger services with shared vehicles","\u003Cp>This document provides guidance on the staged implementation of Electric, Connected and Autonomous Vehicle (eCAV) passenger and delivery services, with a special focus on on-demand responsive passenger services with shared vehicles. This document aims to accelerate innovation and deliver smart transportation by eCAV, in and between cities.\u003C\u002Fp>","2022","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F69254.html","2026-01-27T11:58:22.609Z","2026-01-27T11:58:22.651Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":176},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":177,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":179,"documentId":180,"title":181,"createdAt":182,"updatedAt":182,"publishedAt":183},17,"nurkh0ehpb1cd6uhwnjoevh9","Smart Cities","2026-01-26T14:27:04.971Z","2026-01-26T14:27:04.996Z",{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[186,192,198,204,210,216],{"id":187,"documentId":188,"title":189,"createdAt":190,"updatedAt":190,"publishedAt":191},52,"yq0hxkrdccced1sr51nzb17r","Smart community infrastructures","2026-01-26T23:39:24.834Z","2026-01-26T23:39:24.931Z",{"id":193,"documentId":194,"title":195,"createdAt":196,"updatedAt":196,"publishedAt":197},53,"ukig0edf9jnh355g4icya3jf","Smart transportation","2026-01-26T23:39:24.838Z","2026-01-26T23:39:24.935Z",{"id":199,"documentId":200,"title":201,"createdAt":202,"updatedAt":202,"publishedAt":203},54,"g5kfcb8avomnfdv0u30bia47","Electric Connected and Autonomous Vehicles (eCAVs)","2026-01-26T23:39:24.841Z","2026-01-26T23:39:24.939Z",{"id":205,"documentId":206,"title":207,"createdAt":208,"updatedAt":208,"publishedAt":209},55,"ae85ua9jnmtg49kmrhh26i8t","On-demand responsive passenger services","2026-01-26T23:39:24.844Z","2026-01-26T23:39:24.943Z",{"id":211,"documentId":212,"title":213,"createdAt":214,"updatedAt":214,"publishedAt":215},56,"lhbqn3szadebs4lg4s9q2ofl","Shared vehicles","2026-01-26T23:39:24.847Z","2026-01-26T23:39:24.946Z",{"id":217,"documentId":218,"title":219,"createdAt":220,"updatedAt":220,"publishedAt":221},57,"nn9zwsw5wnieunnuu26z06ei","Implementation guidance","2026-01-26T23:39:24.851Z","2026-01-26T23:39:24.950Z",{"id":223,"documentId":224,"number":225,"title":226,"part":11,"abstract":227,"lastUpdatedYear":171,"linkToOfficial":228,"createdAt":229,"updatedAt":230,"publishedAt":231,"featured":11,"image":11,"development_stage":232,"domain":235,"issuing_body":236,"ai_standard_keywords":237},29,"taw23pk0ztmelkn3b0a51x11","ISO 37181:2022","Smart community infrastructures — Smart transportation by autonomous vehicles on public roads","\u003Cp>This document describes the concept and goals of smart transportation by autonomous vehicles on public roads. It provides guidelines for the successful introduction and organisation of autonomous vehicles, with the aim of enhancing the safety of public road transportation and addressing the challenges to cities such as an aging population and diverse travel demands.\u003C\u002Fp>\u003Cp>This document focuses on the deployment of autonomous vehicles as an operational system for actual use on public roads. This document is intended for those in academia, autonomous vehicle developers, policy makers, research institutions, road infrastructure operators, public road administrators, testing inspection and certification bodies, and vehicle manufacturers.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F69267.html","2026-01-27T11:59:07.085Z","2026-01-27T12:00:57.429Z","2026-01-27T12:00:57.485Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":233},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":234,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":179,"documentId":180,"title":181,"createdAt":182,"updatedAt":182,"publishedAt":183},{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[238,239,240,246,252,253,259,264],{"id":187,"documentId":188,"title":189,"createdAt":190,"updatedAt":190,"publishedAt":191},{"id":193,"documentId":194,"title":195,"createdAt":196,"updatedAt":196,"publishedAt":197},{"id":241,"documentId":242,"title":243,"createdAt":244,"updatedAt":244,"publishedAt":245},58,"w3pytyttf8fkigvwmgn3gtm9","Autonomous vehicles","2026-01-26T23:39:24.854Z","2026-01-26T23:39:24.953Z",{"id":247,"documentId":248,"title":249,"createdAt":250,"updatedAt":250,"publishedAt":251},77,"l7im1drn6r9aw7wusj8drc9e","Public roads","2026-01-26T23:39:25.003Z","2026-01-26T23:39:25.131Z",{"id":112,"documentId":113,"title":114,"createdAt":115,"updatedAt":115,"publishedAt":116},{"id":254,"documentId":255,"title":256,"createdAt":257,"updatedAt":257,"publishedAt":258},78,"vs4tjvxv3gnqboqg3pqgw0l3","Deployment","2026-01-26T23:39:25.031Z","2026-01-26T23:39:25.195Z",{"id":14,"documentId":260,"title":261,"createdAt":262,"updatedAt":262,"publishedAt":263},"rqniqp5ffpdxdb2vpp1146vz","Operational system","2026-01-26T23:39:25.047Z","2026-01-26T23:39:25.149Z",{"id":265,"documentId":266,"title":267,"createdAt":268,"updatedAt":268,"publishedAt":269},70,"ju88z2y2tew7xxefewbuy0wo","Guidelines","2026-01-26T23:39:25.051Z","2026-01-26T23:39:25.152Z",{"id":271,"documentId":272,"number":273,"title":274,"part":11,"abstract":275,"lastUpdatedYear":276,"linkToOfficial":277,"createdAt":278,"updatedAt":278,"publishedAt":279,"featured":11,"image":11,"development_stage":280,"domain":283,"issuing_body":284,"ai_standard_keywords":285},31,"ez7b014yphyy4x7fes4fm1kt","ISO 39003:2023","Road traffic safety (RTS) — Guidance on ethical considerations relating to safety for autonomous vehicles","\u003Cp>This document gives guidance on ethical considerations with regards to road traffic safety of autonomous vehicles (AVs).\u003C\u002Fp>\u003Cp>It is applicable to vehicles in level 5 mode according to SAE J3016 in 2022, as part of its report.\u003C\u002Fp>\u003Cp>This document does not apply to the technical method used to control the decision-making process, nor does it give any guidance on the desired outcomes of those decisions; it gives guidance on ethical aspects for consideration in the design of decision-making process.\u003C\u002Fp>\u003Cp>This document does not set requirements for the outcomes of ethical decisions, nor does it offer guidance on methodology. It only details aspects of the behaviour of AVs for which considerations may be made by the designer\u002Fmanufacturer to ensure that key aspects are not overlooked or disregarded.\u003C\u002Fp>\u003Cp>This document does not offer the technical precision to prescribe the required controls but would, rather, offer a set of “protocol guidelines” that all decision makers regarding automated driving could choose to self-certify against to assure that the desired necessary ethical considerations were addressed during design and effectively controlled.\u003C\u002Fp>","2023","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F50665.html","2026-01-27T12:05:27.025Z","2026-01-27T12:05:27.062Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":281},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":282,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":179,"documentId":180,"title":181,"createdAt":182,"updatedAt":182,"publishedAt":183},{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[286,292,298,304,305,311,317],{"id":287,"documentId":288,"title":289,"createdAt":290,"updatedAt":290,"publishedAt":291},71,"mpf3azvu5c302s377u17ojvo","Road traffic safety (RTS)","2026-01-26T23:39:25.055Z","2026-01-26T23:39:25.156Z",{"id":293,"documentId":294,"title":295,"createdAt":296,"updatedAt":296,"publishedAt":297},72,"qbsdc22eofhm4c5zfyaigfwi","Ethical considerations","2026-01-26T23:39:25.058Z","2026-01-26T23:39:25.159Z",{"id":299,"documentId":300,"title":301,"createdAt":302,"updatedAt":302,"publishedAt":303},73,"wuy49xp86i94uvsskyikzach","Autonomous vehicles (AVs)","2026-01-26T23:39:25.062Z","2026-01-26T23:39:25.163Z",{"id":112,"documentId":113,"title":114,"createdAt":115,"updatedAt":115,"publishedAt":116},{"id":306,"documentId":307,"title":308,"createdAt":309,"updatedAt":309,"publishedAt":310},75,"mq400u6dten5b4616pmwjsho","Design","2026-01-26T23:39:25.066Z","2026-01-26T23:39:25.166Z",{"id":312,"documentId":313,"title":314,"createdAt":315,"updatedAt":315,"publishedAt":316},74,"vq9t0hztiviixhmrvfowz0xz","Decision-making process","2026-01-26T23:39:25.071Z","2026-01-26T23:39:25.169Z",{"id":318,"documentId":319,"title":320,"createdAt":321,"updatedAt":321,"publishedAt":322},76,"i8wf6uqr7ggsm2k859smnxt7","Protocol guidelines","2026-01-26T23:39:25.078Z","2026-01-26T23:39:25.173Z",{"id":100,"documentId":324,"number":325,"title":326,"part":327,"abstract":328,"lastUpdatedYear":329,"linkToOfficial":330,"createdAt":331,"updatedAt":332,"publishedAt":333,"featured":11,"image":11,"development_stage":334,"domain":337,"issuing_body":343,"ai_standard_keywords":344},"s005iyxbvane024gf57nsgpz","ISO 16300-1:2018","Automation systems and integration — Interoperability of capability units for manufacturing application solutions","Part 1: Interoperability criteria of capability units per application requirements","\u003Cp>This document specifies a framework for describing an automation solution in terms of a set of capabilities provided by a set of manufacturing software units.\u003C\u002Fp>\u003Cp>The framework also defines a set of capability elements and composition rules to represent the interoperability criteria in terms of the automation system capability requirements of an enterprise application.\u003C\u002Fp>","2018","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F64684.html","2026-01-27T12:05:49.851Z","2026-01-27T12:07:57.554Z","2026-01-27T12:07:57.606Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":335},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":336,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":338,"documentId":339,"title":340,"createdAt":341,"updatedAt":341,"publishedAt":342},15,"cssn88taa797xh6xon75bqu2","Manufacturing","2026-01-26T14:26:54.185Z","2026-01-26T14:26:54.213Z",{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[345,351,357,363,369,375,381,387],{"id":346,"documentId":347,"title":348,"createdAt":349,"updatedAt":349,"publishedAt":350},89,"d97n4x72mjh4m5sogw0oqp7r","Automation systems","2026-01-26T23:39:25.247Z","2026-01-26T23:39:25.329Z",{"id":352,"documentId":353,"title":354,"createdAt":355,"updatedAt":355,"publishedAt":356},90,"aq7eemqx6hz3ypy6tbacb105","Interoperability","2026-01-26T23:39:25.250Z","2026-01-26T23:39:25.332Z",{"id":358,"documentId":359,"title":360,"createdAt":361,"updatedAt":361,"publishedAt":362},91,"ccb00clsclbx2gy7k8d8lq2m","Capability units","2026-01-26T23:39:25.254Z","2026-01-26T23:39:25.334Z",{"id":364,"documentId":365,"title":366,"createdAt":367,"updatedAt":367,"publishedAt":368},92,"pw9chc779brykxxii8padv5i","Manufacturing application solutions","2026-01-26T23:39:25.258Z","2026-01-26T23:39:25.337Z",{"id":370,"documentId":371,"title":372,"createdAt":373,"updatedAt":373,"publishedAt":374},93,"totlg388x77x38n8g2kvxjlx","Framework","2026-01-26T23:39:25.261Z","2026-01-26T23:39:25.343Z",{"id":376,"documentId":377,"title":378,"createdAt":379,"updatedAt":379,"publishedAt":380},94,"vadig354fi1rmeo2u0a6wstg","Capability elements","2026-01-26T23:39:25.271Z","2026-01-26T23:39:25.361Z",{"id":382,"documentId":383,"title":384,"createdAt":385,"updatedAt":385,"publishedAt":386},102,"d5m0x957c37s9rzrno0bguc9","Composition rules","2026-01-26T23:39:25.290Z","2026-01-26T23:39:25.410Z",{"id":388,"documentId":389,"title":390,"createdAt":391,"updatedAt":391,"publishedAt":392},105,"eyoane4dpdbjzrtdk8t7qe7y","Enterprise application","2026-01-26T23:39:25.308Z","2026-01-26T23:39:25.447Z",{"id":112,"documentId":394,"number":395,"title":326,"part":396,"abstract":397,"lastUpdatedYear":398,"linkToOfficial":399,"createdAt":400,"updatedAt":401,"publishedAt":402,"featured":11,"image":11,"development_stage":403,"domain":406,"issuing_body":407,"ai_standard_keywords":408},"i8n2duh0jlx7li6smvp0undx","ISO 16300-2:2019","Part 2: Capability templates and software unit cataloguing","\u003Cp>This document specifies a set of template definitions to describe the capability of a software unit of an automation solution that can be mapped to the functional requirements of a target manufacturing application.\u003C\u002Fp>\u003Cp>This document specifies how to develop and manage a software unit catalogue in terms of capability properties and defines mapping rules from capability profiles to a software unit catalogue.\u003C\u002Fp>","2019","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F64685.html","2026-01-27T12:08:22.972Z","2026-01-27T12:10:17.822Z","2026-01-27T12:10:17.866Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":404},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":405,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":338,"documentId":339,"title":340,"createdAt":341,"updatedAt":341,"publishedAt":342},{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[409,410,411,412,413,419,424,430],{"id":346,"documentId":347,"title":348,"createdAt":349,"updatedAt":349,"publishedAt":350},{"id":352,"documentId":353,"title":354,"createdAt":355,"updatedAt":355,"publishedAt":356},{"id":358,"documentId":359,"title":360,"createdAt":361,"updatedAt":361,"publishedAt":362},{"id":364,"documentId":365,"title":366,"createdAt":367,"updatedAt":367,"publishedAt":368},{"id":414,"documentId":415,"title":416,"createdAt":417,"updatedAt":417,"publishedAt":418},95,"mpekmwzyi0y0zzvoeqzn99xe","Capability templates","2026-01-26T23:39:25.305Z","2026-01-26T23:39:25.383Z",{"id":32,"documentId":420,"title":421,"createdAt":422,"updatedAt":422,"publishedAt":423},"i95jp27a7f59nnch81ok8soh","Software unit catalogue","2026-01-26T23:39:25.314Z","2026-01-26T23:39:25.392Z",{"id":425,"documentId":426,"title":427,"createdAt":428,"updatedAt":428,"publishedAt":429},108,"v39vk1sjtv338k7aayd94lcq","Mapping rules","2026-01-26T23:39:25.417Z","2026-01-26T23:39:25.498Z",{"id":431,"documentId":432,"title":433,"createdAt":434,"updatedAt":434,"publishedAt":435},109,"k1ry8iuz5gjv3fe01pxyqq4f","Capability properties","2026-01-26T23:39:25.420Z","2026-01-26T23:39:25.501Z",{"id":124,"documentId":437,"number":438,"title":326,"part":439,"abstract":440,"lastUpdatedYear":441,"linkToOfficial":442,"createdAt":443,"updatedAt":444,"publishedAt":445,"featured":11,"image":11,"development_stage":446,"domain":449,"issuing_body":450,"ai_standard_keywords":451},"txofsv2rhrxa5s9js9o2mprt","ISO 16300-3:2017","Part 3: Verification and validation of interoperability among capability units","\u003Cp>ISO 16300‑3:2017 specifies a framework for verifying and validating the interoperability of manufacturing capability units (MCUs) having a set of capabilities that meet the functional requirements of a target manufacturing application solution.\u003C\u002Fp>\u003Cp>The verification and validation framework describes the use of the interoperability criteria in ISO 16300‑1 and the steps to be performed.\u003C\u002Fp>","2017","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F53763.html","2026-01-27T12:10:28.507Z","2026-01-27T12:12:21.021Z","2026-01-27T12:12:21.054Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":447},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":448,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":338,"documentId":339,"title":340,"createdAt":341,"updatedAt":341,"publishedAt":342},{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[452,453,454,455,456,462,468,474],{"id":346,"documentId":347,"title":348,"createdAt":349,"updatedAt":349,"publishedAt":350},{"id":352,"documentId":353,"title":354,"createdAt":355,"updatedAt":355,"publishedAt":356},{"id":358,"documentId":359,"title":360,"createdAt":361,"updatedAt":361,"publishedAt":362},{"id":364,"documentId":365,"title":366,"createdAt":367,"updatedAt":367,"publishedAt":368},{"id":457,"documentId":458,"title":459,"createdAt":460,"updatedAt":460,"publishedAt":461},110,"zjt8x1sw25cnec9v0btrcyw7","Verification and validation","2026-01-26T23:39:25.423Z","2026-01-26T23:39:25.505Z",{"id":463,"documentId":464,"title":465,"createdAt":466,"updatedAt":466,"publishedAt":467},111,"dhlyiaz8m9zsnmjad4q0iva0","Manufacturing capability units (MCUs)","2026-01-26T23:39:25.426Z","2026-01-26T23:39:25.508Z",{"id":469,"documentId":470,"title":471,"createdAt":472,"updatedAt":472,"publishedAt":473},112,"ttiwzy8m22bg0ssg66exheao","Functional requirements","2026-01-26T23:39:25.438Z","2026-01-26T23:39:25.528Z",{"id":370,"documentId":371,"title":372,"createdAt":373,"updatedAt":373,"publishedAt":374},{"id":142,"documentId":476,"number":477,"title":478,"part":479,"abstract":480,"lastUpdatedYear":398,"linkToOfficial":481,"createdAt":482,"updatedAt":483,"publishedAt":484,"featured":11,"image":11,"development_stage":485,"domain":488,"issuing_body":489,"ai_standard_keywords":490},"qni8efby9dm3a884wpivzrjt","ISO 16300-4:2019","Automation systems and integration — Interoperability of capability units for manufacturing application solutions ","Part 4: Capability unit assessment for the manufacturing application requirements","\u003Cp>This document specifies the search methodology from software unit catalogues for acquiring candidate capability units which satisfy the manufacturing application requirements.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F71103.html","2026-01-27T12:13:55.014Z","2026-01-27T12:15:36.717Z","2026-01-27T12:15:36.771Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":486},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":487,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":338,"documentId":339,"title":340,"createdAt":341,"updatedAt":341,"publishedAt":342},{"id":93,"documentId":94,"title":95,"createdAt":96,"updatedAt":96,"publishedAt":97},[491,492,493,494,495,501,507,513],{"id":346,"documentId":347,"title":348,"createdAt":349,"updatedAt":349,"publishedAt":350},{"id":352,"documentId":353,"title":354,"createdAt":355,"updatedAt":355,"publishedAt":356},{"id":358,"documentId":359,"title":360,"createdAt":361,"updatedAt":361,"publishedAt":362},{"id":364,"documentId":365,"title":366,"createdAt":367,"updatedAt":367,"publishedAt":368},{"id":496,"documentId":497,"title":498,"createdAt":499,"updatedAt":499,"publishedAt":500},113,"qeniwknl2tgojr44nb23uor5","Capability unit assessment","2026-01-26T23:39:25.446Z","2026-01-26T23:39:25.545Z",{"id":502,"documentId":503,"title":504,"createdAt":505,"updatedAt":505,"publishedAt":506},114,"l5u5x15c7jkji65klt6y6tp5","Search methodology","2026-01-26T23:39:25.482Z","2026-01-26T23:39:25.565Z",{"id":508,"documentId":509,"title":510,"createdAt":511,"updatedAt":511,"publishedAt":512},122,"jg9qtif629eysri3uctk2s4z","Software unit catalogues","2026-01-26T23:39:25.520Z","2026-01-26T23:39:25.652Z",{"id":514,"documentId":515,"title":516,"createdAt":517,"updatedAt":517,"publishedAt":518},118,"y3micpd4uctn2belmwi94tz2","Application requirements","2026-01-26T23:39:25.517Z","2026-01-26T23:39:25.593Z",{"id":130,"documentId":520,"number":521,"title":522,"part":11,"abstract":523,"lastUpdatedYear":524,"linkToOfficial":525,"createdAt":526,"updatedAt":527,"publishedAt":528,"featured":11,"image":11,"development_stage":529,"domain":532,"issuing_body":538,"ai_standard_keywords":544},"u1et15piho3573emksa64j7j","ISO\u002FIEC 9868:2025","Information technology — Design, development, use and maintenance of biometric identification systems involving passive capture subjects","\u003Cp>This document provides recommendations and requirements for the design, development, use and maintenance of biometric identification systems involving passive capture subjects, including pre- and post-deployment evaluation.\u003C\u002Fp>\u003Cp>While the emphasis is on surveillance systems, this document is also applicable to other types of biometric identification systems involving passive capture subjects, regardless of biometric characteristic or sensing technology. This includes systems involving passive capture of subjects where some capture subjects enrolled voluntarily.\u003C\u002Fp>\u003Cp>This document does not apply to biometric verification systems and biometric identification systems only involving capture subjects deliberately taking part in the capture.\u003C\u002Fp>\u003Cp>This document does not define specific services, platforms or tools.\u003C\u002Fp>","2025","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F83613.html","2026-01-27T12:15:47.127Z","2026-01-27T12:18:00.477Z","2026-01-27T12:18:00.564Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":530},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":531,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},"xih6hrdkr3ieqd4a59zoy8bb","Information Technology \u002F ICT","2026-01-26T14:26:15.922Z","2026-01-26T14:26:30.882Z","2026-01-26T14:26:30.922Z",{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},6,"rsuv59tvi4up8gqa2ue4xqv2","ISO\u002FIEC","2026-01-26T14:23:22.583Z","2026-01-26T14:23:22.608Z",[545,551,557,558,564,570,576,582,588],{"id":546,"documentId":547,"title":548,"createdAt":549,"updatedAt":549,"publishedAt":550},119,"t9yzsek1dq4ooyej7jqw7yhk","Biometric identification systems","2026-01-26T23:39:25.602Z","2026-01-26T23:39:25.666Z",{"id":552,"documentId":553,"title":554,"createdAt":555,"updatedAt":555,"publishedAt":556},120,"f1f4523u4y4xd7v2iqh6ljbt","Passive capture subjects","2026-01-26T23:39:25.605Z","2026-01-26T23:39:25.670Z",{"id":306,"documentId":307,"title":308,"createdAt":309,"updatedAt":309,"publishedAt":310},{"id":559,"documentId":560,"title":561,"createdAt":562,"updatedAt":562,"publishedAt":563},121,"ui3gwkqk225905z4t9c352aa","Development","2026-01-26T23:39:25.609Z","2026-01-26T23:39:25.675Z",{"id":565,"documentId":566,"title":567,"createdAt":568,"updatedAt":568,"publishedAt":569},141,"hum1ar7j5dwhsniq15od91md","Use","2026-01-26T23:39:25.909Z","2026-01-26T23:39:26.076Z",{"id":571,"documentId":572,"title":573,"createdAt":574,"updatedAt":574,"publishedAt":575},149,"qr6kv3sqzy0cn38hup037g78","Maintenance","2026-01-26T23:39:25.914Z","2026-01-26T23:39:26.111Z",{"id":577,"documentId":578,"title":579,"createdAt":580,"updatedAt":580,"publishedAt":581},133,"m0gvzqmzku6cfjzbpjsfui97","Surveillance systems","2026-01-26T23:39:25.918Z","2026-01-26T23:39:26.016Z",{"id":583,"documentId":584,"title":585,"createdAt":586,"updatedAt":586,"publishedAt":587},134,"tmjg439uufegc9g3m0qamecs","Biometric characteristic","2026-01-26T23:39:25.922Z","2026-01-26T23:39:26.020Z",{"id":589,"documentId":590,"title":591,"createdAt":592,"updatedAt":592,"publishedAt":593},135,"t851ldqi5zjvcme2r86jauij","Sensing technology","2026-01-26T23:39:25.926Z","2026-01-26T23:39:26.030Z",{"id":595,"documentId":596,"number":597,"title":598,"part":11,"abstract":599,"lastUpdatedYear":276,"linkToOfficial":600,"createdAt":601,"updatedAt":602,"publishedAt":603,"featured":11,"image":11,"development_stage":604,"domain":607,"issuing_body":608,"ai_standard_keywords":609},43,"mqni3xh6689px0zlltp8aus8","ISO\u002FIEC 24661:2023","Information technology — User interfaces — Full duplex speech interaction","\u003Cp>This document specifies user interfaces (UIs) designed for full duplex (FDX) speech interaction. It also specifies the FDX speech interaction model, features, functional components and requirements, thus providing a framework to support natural conversational interfaces between humans and machines. It also provides privacy considerations for applying FDX speech interaction.\u003C\u002Fp>\u003Cp>This document is applicable to UIs for speech interaction and communication protocols for setting up a session-oriented FDX interaction between humans and machines.\u003C\u002Fp>\u003Cp>This document does not define the speech interaction engines themselves or specify the details of specific engines, devices and approaches.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F79186.html","2026-01-27T12:18:15.556Z","2026-01-27T12:19:58.524Z","2026-01-27T12:19:58.563Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":605},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":606,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[610,616,622,628,634,640,646],{"id":611,"documentId":612,"title":613,"createdAt":614,"updatedAt":614,"publishedAt":615},136,"gvxs74gg6mzg3760ibjvei8w","User interfaces (UIs)","2026-01-26T23:39:25.931Z","2026-01-26T23:39:26.039Z",{"id":617,"documentId":618,"title":619,"createdAt":620,"updatedAt":620,"publishedAt":621},137,"pwia80513uypi4kl9o2w52yd","Full duplex (FDX) speech interaction","2026-01-26T23:39:25.935Z","2026-01-26T23:39:26.043Z",{"id":623,"documentId":624,"title":625,"createdAt":626,"updatedAt":626,"publishedAt":627},138,"bnva8zb3rxzj3bnbsd3s0gk8","Conversational interfaces","2026-01-26T23:39:25.939Z","2026-01-26T23:39:26.047Z",{"id":629,"documentId":630,"title":631,"createdAt":632,"updatedAt":632,"publishedAt":633},139,"ffs76bqyxkq3njgcste6znyn","Speech interaction model","2026-01-26T23:39:25.943Z","2026-01-26T23:39:26.051Z",{"id":635,"documentId":636,"title":637,"createdAt":638,"updatedAt":638,"publishedAt":639},140,"uszibl3ilkqyluekfuan0gnc","Functional components","2026-01-26T23:39:25.948Z","2026-01-26T23:39:26.055Z",{"id":641,"documentId":642,"title":643,"createdAt":644,"updatedAt":644,"publishedAt":645},153,"r3erbcgl351pue4tfq773sm1","Requirements","2026-01-26T23:39:26.131Z","2026-01-26T23:39:26.209Z",{"id":647,"documentId":648,"title":649,"createdAt":650,"updatedAt":650,"publishedAt":651},154,"mtfjdapkfsmhwpm3i9qcp3ww","Privacy considerations","2026-01-26T23:39:26.135Z","2026-01-26T23:39:26.212Z",{"id":653,"documentId":654,"number":655,"title":656,"part":657,"abstract":658,"lastUpdatedYear":659,"linkToOfficial":660,"createdAt":661,"updatedAt":662,"publishedAt":663,"featured":11,"image":11,"development_stage":664,"domain":667,"issuing_body":668,"ai_standard_keywords":669},45,"uauraonclh986w67c7qcaahg","ISO\u002FIEC 15938-1:2002","Information technology — Multimedia content description interface","Part 1: Systems","\u003Cp>This International Standard defines a Multimedia Content Description Interface, specifying a series of interfaces from system to application level to allow disparate systems to interchange information about multimedia content. It describes the architecture for systems, a language for extensions and specific applications, description tools in the audio and visual domains, as well as tools that are not specific to audio-visual domains. This part of ISO\u002FIEC 15938 specifies system level functionalities for the communication of multimedia content descriptions. ISO\u002FIEC 15938-1 provides a specification which will: enable development of ISO\u002FIEC 15938 receiving sub-systems, called ISO\u002FIEC 15938 Terminal, or Terminal in short, to receive and assemble possibly partitioned and compressed multimedia content descriptions provide rules for the preparation of multimedia content descriptions consisting of the tools specified in Parts 3, 4 and 5 of ISO\u002FIEC 15938 for efficient transport and storage. The decoding process within the ISO\u002FIEC 15938 Terminal is normative. The rules mentioned provide guidance for the preparation and encoding of multimedia content descriptions without leading to a unique encoded representation of such descriptions. This part of the MPEG-7 Standard is intended to be implemented in conjunction with other parts of the standard. In particular, MPEG-7 Part 1: Systems assumes some knowledge of Part 2: Description Definition Language (DDL) in its normative syntactic definitions of Descriptors and Description Schemes, as well as in the processing of schema and descriptions. The methods for obtaining the descriptions to which the encoding techniques in this part refer are defined in Parts 3, 4, and 5 of ISO\u002FIEC 15938. MPEG-7 is an extensible standard. The standard method of extending the standard beyond the Description Schemes provided in the standard is to define new ones in the DDL, and to make those DSs as accessible as the instantiated descriptions. Further details are available in Part 2.\u003C\u002Fp>","2002","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F34228.html#amendment","2026-01-27T12:20:09.430Z","2026-01-27T12:22:56.174Z","2026-01-27T12:22:56.334Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":665},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":666,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[670,676,682,688,693,698,704,710,716],{"id":671,"documentId":672,"title":673,"createdAt":674,"updatedAt":674,"publishedAt":675},155,"z3ijfxhi8ezv068l4ft3c5s2","Multimedia Content Description Interface","2026-01-26T23:39:26.138Z","2026-01-26T23:39:26.219Z",{"id":677,"documentId":678,"title":679,"createdAt":680,"updatedAt":680,"publishedAt":681},156,"rycrmh48x5ifdxqsrrlsmvfj","MPEG-7","2026-01-26T23:39:26.141Z","2026-01-26T23:39:26.222Z",{"id":683,"documentId":684,"title":685,"createdAt":686,"updatedAt":686,"publishedAt":687},157,"mi3tkhgqozz5lct6bnt2n30k","Systems","2026-01-26T23:39:26.145Z","2026-01-26T23:39:26.225Z",{"id":45,"documentId":689,"title":690,"createdAt":691,"updatedAt":691,"publishedAt":692},"ymdemfv13rb4tk6895nb6qvn","Multimedia content descriptions","2026-01-26T23:39:26.148Z","2026-01-26T23:39:26.228Z",{"id":50,"documentId":694,"title":695,"createdAt":696,"updatedAt":696,"publishedAt":697},"u8hoeuiefdzgt1n3ykcstii4","Architecture","2026-01-26T23:39:26.161Z","2026-01-26T23:39:26.245Z",{"id":699,"documentId":700,"title":701,"createdAt":702,"updatedAt":702,"publishedAt":703},168,"gseds7nwj8j189b0nyzfq9lk","Communication","2026-01-26T23:39:26.184Z","2026-01-26T23:39:26.294Z",{"id":705,"documentId":706,"title":707,"createdAt":708,"updatedAt":708,"publishedAt":709},169,"nmf8l1pbipul5bdoi4iadfr9","Descriptors","2026-01-26T23:39:26.254Z","2026-01-26T23:39:26.384Z",{"id":711,"documentId":712,"title":713,"createdAt":714,"updatedAt":714,"publishedAt":715},167,"ks7lrjtvhd80mg8k40xxlbzl","Description Schemes","2026-01-26T23:39:26.251Z","2026-01-26T23:39:26.335Z",{"id":717,"documentId":718,"title":719,"createdAt":720,"updatedAt":720,"publishedAt":721},170,"v8h43ncu5d2yml1l1iozjvey","Description Definition Language (DDL)","2026-01-26T23:39:26.311Z","2026-01-26T23:39:26.404Z",{"id":723,"documentId":724,"number":725,"title":656,"part":726,"abstract":727,"lastUpdatedYear":659,"linkToOfficial":728,"createdAt":729,"updatedAt":730,"publishedAt":731,"featured":11,"image":11,"development_stage":732,"domain":735,"issuing_body":736,"ai_standard_keywords":737},47,"tis60lewocd40o23597oluvw","ISO\u002FIEC 15938-2:2002","Part 2: Description definition language","\u003Cp>This International Standard specifies a metadata system for describing multimedia content. It specifies the Description Definition Language (DDL) that comprises part 2 of the standard (ISO\u002FIEC 15938-2). The goal of this part of the MPEG-7 International Standard is to specify a language that will enable MPEG-7 users and developers to: create valid MPEG-7 description schemes and descriptors; develop tools such as editors and parsers for processing descriptions, description schemes and descriptors; generate refinements, extensions and modifications to the DDL. This International Standard describes the features of the DDL. It defines the syntax of the DDL constructs and datatypes and provides optional (informative) examples that illustrate the application of the DDL to the specification and instantiation of MPEG-7 descriptions.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F34229.html","2026-01-27T12:23:34.625Z","2026-01-27T12:26:03.167Z","2026-01-27T12:26:03.221Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":733},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":734,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[738,744,750,751,752,758,759,765,771],{"id":739,"documentId":740,"title":741,"createdAt":742,"updatedAt":742,"publishedAt":743},171,"fzazuybjape1kujaynah32u7","Multimedia content","2026-01-26T23:39:26.317Z","2026-01-26T23:39:26.407Z",{"id":745,"documentId":746,"title":747,"createdAt":748,"updatedAt":748,"publishedAt":749},172,"hqlkhberorj4fljlv4yykcip","Metadata system","2026-01-26T23:39:26.321Z","2026-01-26T23:39:26.410Z",{"id":717,"documentId":718,"title":719,"createdAt":720,"updatedAt":720,"publishedAt":721},{"id":677,"documentId":678,"title":679,"createdAt":680,"updatedAt":680,"publishedAt":681},{"id":753,"documentId":754,"title":755,"createdAt":756,"updatedAt":756,"publishedAt":757},174,"rw2555u42ulp6lkssjxnnt45","Description schemes","2026-01-26T23:39:26.325Z","2026-01-26T23:39:26.418Z",{"id":705,"documentId":706,"title":707,"createdAt":708,"updatedAt":708,"publishedAt":709},{"id":760,"documentId":761,"title":762,"createdAt":763,"updatedAt":763,"publishedAt":764},173,"tjv937vn8k0sf2pykgupcflh","Syntax","2026-01-26T23:39:26.329Z","2026-01-26T23:39:26.423Z",{"id":766,"documentId":767,"title":768,"createdAt":769,"updatedAt":769,"publishedAt":770},175,"kcay857ul9ola0fw8jsu5qoi","Datatypes","2026-01-26T23:39:26.332Z","2026-01-26T23:39:26.428Z",{"id":772,"documentId":773,"title":774,"createdAt":775,"updatedAt":775,"publishedAt":776},176,"u76wolpvo3ub6ima28mr7xr7","Extensions","2026-01-26T23:39:26.349Z","2026-01-26T23:39:26.470Z",{"id":778,"documentId":779,"number":780,"title":656,"part":781,"abstract":782,"lastUpdatedYear":659,"linkToOfficial":783,"createdAt":784,"updatedAt":785,"publishedAt":786,"featured":11,"image":11,"development_stage":787,"domain":790,"issuing_body":791,"ai_standard_keywords":792},49,"mf0m7op0ugcu5i6fsdehga5o","ISO\u002FIEC 15938-3:2002","Part 3: Visual","\u003Cp>The structure of this document is as follows. Clauses 2-4 specify the terms, abbreviations, symbols and conventions used throughout the document. Clauses 5-11 contain definitions of the description tools standardized by 15938-3 grouped by the visual features they are associated with, starting with basic structures and containers in Clause 5, through color, texture, shape, motion, localization in Clause 10. Clause 11 contains the remaining, unclassified items. Each description tool is described by the following subclauses: Syntax: Normative DDL specification of the Ds or DSs; Binary Syntax: Normative binary representation of the Ds or DSs.; Semantic: Normative definition of the semantics of all the components of the corresponding D or DS.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F34230.html","2026-01-27T12:26:11.578Z","2026-01-27T12:28:42.672Z","2026-01-27T12:28:42.709Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":788},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":789,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[793,799,805,811,817,823,829,835,841,842,848],{"id":794,"documentId":795,"title":796,"createdAt":797,"updatedAt":797,"publishedAt":798},188,"aqu7ggaii2l4hcrmhyq46ndm","Multimedia content description interface","2026-01-26T23:39:26.499Z","2026-01-26T23:39:26.646Z",{"id":800,"documentId":801,"title":802,"createdAt":803,"updatedAt":803,"publishedAt":804},187,"zt7nr18sni6z30gc5p5315gx","Visual features","2026-01-26T23:39:26.512Z","2026-01-26T23:39:26.639Z",{"id":806,"documentId":807,"title":808,"createdAt":809,"updatedAt":809,"publishedAt":810},196,"c5lt6o6hhze2haq03sc73urk","Description tools","2026-01-26T23:39:26.519Z","2026-01-26T23:39:26.680Z",{"id":812,"documentId":813,"title":814,"createdAt":815,"updatedAt":815,"publishedAt":816},189,"kwto8d3y0cdfswnmihfhh55v","Color","2026-01-26T23:39:26.541Z","2026-01-26T23:39:26.684Z",{"id":818,"documentId":819,"title":820,"createdAt":821,"updatedAt":821,"publishedAt":822},190,"s4f5pb02r376zlysr9t53wlu","Texture","2026-01-26T23:39:26.548Z","2026-01-26T23:39:26.687Z",{"id":824,"documentId":825,"title":826,"createdAt":827,"updatedAt":827,"publishedAt":828},191,"xbd4np86va5kxlbzgkaz6yu9","Shape","2026-01-26T23:39:26.555Z","2026-01-26T23:39:26.691Z",{"id":830,"documentId":831,"title":832,"createdAt":833,"updatedAt":833,"publishedAt":834},192,"x839mlkfd502knsmez4p0vu5","Motion","2026-01-26T23:39:26.560Z","2026-01-26T23:39:26.695Z",{"id":836,"documentId":837,"title":838,"createdAt":839,"updatedAt":839,"publishedAt":840},193,"t9l35r2t10hucddsfwl5ibe3","Localization","2026-01-26T23:39:26.564Z","2026-01-26T23:39:26.698Z",{"id":760,"documentId":761,"title":762,"createdAt":763,"updatedAt":763,"publishedAt":764},{"id":843,"documentId":844,"title":845,"createdAt":846,"updatedAt":846,"publishedAt":847},194,"fgi0rbnrdendgkhqlnalegza","Binary representation","2026-01-26T23:39:26.570Z","2026-01-26T23:39:26.701Z",{"id":849,"documentId":850,"title":851,"createdAt":852,"updatedAt":852,"publishedAt":853},195,"kdt2wbml3ip53b3y2p8bf35f","Semantics","2026-01-26T23:39:26.591Z","2026-01-26T23:39:26.720Z",{"id":160,"documentId":855,"number":856,"title":656,"part":857,"abstract":858,"lastUpdatedYear":659,"linkToOfficial":859,"createdAt":860,"updatedAt":861,"publishedAt":862,"featured":11,"image":11,"development_stage":863,"domain":866,"issuing_body":867,"ai_standard_keywords":868},"zd7jtffcawd1ph7h9u104yzl","ISO\u002FIEC 15938-4:2002","Part 4: Audio","\u003Cp>This International Standard defines a Multimedia Content Description Interface, specifying a series of interfaces from system to application level to allow disparate systems to interchange information about multimedia content. It describes the architecture for systems, a language for extensions and specific applications, description tools in the audio and visual domains, as well as tools that are not specific to audio-visual domains. As a whole, this International Standard encompassing all of the aforementioned components is known as \"MPEG-7.\" MPEG-7 is divided into eight parts (as defined in the Foreword). This part of the MPEG-7 Standard (Part 4: Audio) specifies description tools that pertain to multimedia in the audio domain. See below for further details of application. This part of the MPEG-7 Standard is intended to be implemented in conjunction with other parts of the standard. In particular, MPEG-7 Part 4: Audio assumes knowledge of Part 2: Description Definition Language (DDL) in its normative syntactic definitions of Descriptors and Description Schemes. This part of the standard also has dependencies upon clauses in Part 5: Multimedia Description Schemes, namely many of the fundamental Description Schemes that extend the basic type capabilities of the DDL. MPEG-7 is an extensible standard. The method to extend the standard beyond the Description Schemes provided in the standard is to define new ones in the DDL, and to make those DSs available with the instantiated descriptions. Further details are available in Part 2. To avoid duplicate functionality with other parts of the standard, the DDL is the only extension facility provided.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F34231.html","2026-01-27T12:37:59.034Z","2026-01-27T12:40:16.512Z","2026-01-27T12:40:16.572Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":864},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":865,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[869,870,871,877,878,879,880],{"id":671,"documentId":672,"title":673,"createdAt":674,"updatedAt":674,"publishedAt":675},{"id":677,"documentId":678,"title":679,"createdAt":680,"updatedAt":680,"publishedAt":681},{"id":872,"documentId":873,"title":874,"createdAt":875,"updatedAt":875,"publishedAt":876},207,"de1quvtkhqeyk3t04hodhgxu","Audio domain","2026-01-26T23:39:26.773Z","2026-01-26T23:39:26.857Z",{"id":806,"documentId":807,"title":808,"createdAt":809,"updatedAt":809,"publishedAt":810},{"id":50,"documentId":694,"title":695,"createdAt":696,"updatedAt":696,"publishedAt":697},{"id":717,"documentId":718,"title":719,"createdAt":720,"updatedAt":720,"publishedAt":721},{"id":711,"documentId":712,"title":713,"createdAt":714,"updatedAt":714,"publishedAt":715},{"id":193,"documentId":882,"number":883,"title":656,"part":884,"abstract":885,"lastUpdatedYear":886,"linkToOfficial":887,"createdAt":888,"updatedAt":889,"publishedAt":890,"featured":11,"image":11,"development_stage":891,"domain":894,"issuing_body":895,"ai_standard_keywords":896},"ooc7l8zn0gc1mge9dhf94dhw","ISO\u002FIEC 15938-5:2003","Part 5: Multimedia description schemes","\u003Cp>ISO\u002FIEC 15938-5:2003 specifies a metadata system for describing multimedia content. ISO\u002FIEC 15938-5:2003 specifies the Multimedia Description Schemes (MDS) description tools (Description Schemes, Descriptors, and datatypes) that comprise ISO\u002FIEC 15938-5:2003 by specifying the normative syntax of each description tool using Description Definition Language (DDL) (see ISO\u002FIEC 15938-2) and the normative semantics including semantics of each component of each description tool (attributes and elements). ISO\u002FIEC 15938-5:2003 also provides informative examples that illustrate the instantiation of description tools in creating descriptions conforming to ISO\u002FIEC 15938-5:2003. ISO\u002FIEC 15938-5:2003 defines the following basic elements: schema tools (the root element, top-level types, description metadata, and packages), basic datatypes (integers, reals, vectors, matrices), linking and media localization tools (spatial and temporal localization), basic description tools (language, text, classification schemes). ISO\u002FIEC 15938-5:2003 defines the following content description tools, which describe the features of the multimedia content and the immutable metadata related to the multimedia content: structure description tools (spatio-temporal segments of multimedia content) and semantic description tools (\"real-world\" semantics related to or captured by the multimedia content, including objects, events, concepts, and so forth). ISO\u002FIEC 15938-5:2003 defines additionally the following description tools for content metadata: media description (storage format, encoding), creation and production (title, creator, classification, purpose), and usage (access rights, publication). The content description and metadata tools are related in the sense that the content description tools use the content metadata tools. For example, a description of creation and production or media information can be attached to an individual video or video segment in order to describe the structure and creation and production of the multimedia content. ISO\u002FIEC 15938-5:2003 also defines description tools for navigation and access (browsing, summarization), content organization (collections and models), and user interaction (user preferences pertaining to consumption of multimedia and usage history). Finally, ISO\u002FIEC 15938-5:2003 defines classification schemes, which organize terms that are used by the description tools.\u003C\u002Fp>","2003","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F34232.html","2026-01-27T12:40:26.555Z","2026-01-27T12:43:04.631Z","2026-01-27T12:43:04.682Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":892},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":893,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[897,903,904,905,911,917,923,929,935,941,947],{"id":898,"documentId":899,"title":900,"createdAt":901,"updatedAt":901,"publishedAt":902},208,"yn2ziwdlzt3bwqmowpnbvnxm","Multimedia description schemes (MDS)","2026-01-26T23:39:26.785Z","2026-01-26T23:39:26.887Z",{"id":745,"documentId":746,"title":747,"createdAt":748,"updatedAt":748,"publishedAt":749},{"id":806,"documentId":807,"title":808,"createdAt":809,"updatedAt":809,"publishedAt":810},{"id":906,"documentId":907,"title":908,"createdAt":909,"updatedAt":909,"publishedAt":910},209,"xnia9hzog8lt4m2i2u8oojie","DDL","2026-01-26T23:39:26.791Z","2026-01-26T23:39:26.891Z",{"id":912,"documentId":913,"title":914,"createdAt":915,"updatedAt":915,"publishedAt":916},210,"ahimt1t9ynoj8gkst4m9dh54","Basic datatypes","2026-01-26T23:39:26.794Z","2026-01-26T23:39:26.894Z",{"id":918,"documentId":919,"title":920,"createdAt":921,"updatedAt":921,"publishedAt":922},211,"fufvy5vssyavnt3ujhm610qv","Linking","2026-01-26T23:39:26.799Z","2026-01-26T23:39:26.898Z",{"id":924,"documentId":925,"title":926,"createdAt":927,"updatedAt":927,"publishedAt":928},212,"yxc4drpg95pvy8g43st1w2k1","Media localization","2026-01-26T23:39:26.803Z","2026-01-26T23:39:26.901Z",{"id":930,"documentId":931,"title":932,"createdAt":933,"updatedAt":933,"publishedAt":934},213,"pd6v7hpiqxa10xsmy9t5yz16","Content description","2026-01-26T23:39:26.808Z","2026-01-26T23:39:26.926Z",{"id":936,"documentId":937,"title":938,"createdAt":939,"updatedAt":939,"publishedAt":940},218,"nmjux8yuor5s81z4mwk4d7ih","Content metadata","2026-01-26T23:39:26.829Z","2026-01-26T23:39:26.982Z",{"id":942,"documentId":943,"title":944,"createdAt":945,"updatedAt":945,"publishedAt":946},214,"y3xb4t6kpk3x3hbvjc0bu799","Navigation and access","2026-01-26T23:39:26.823Z","2026-01-26T23:39:26.938Z",{"id":948,"documentId":949,"title":950,"createdAt":951,"updatedAt":951,"publishedAt":952},219,"xjx2n9yvg9vklgqnf3idiwa1","Classification schemes","2026-01-26T23:39:26.883Z","2026-01-26T23:39:27.028Z",{"id":205,"documentId":954,"number":955,"title":656,"part":956,"abstract":957,"lastUpdatedYear":958,"linkToOfficial":959,"createdAt":960,"updatedAt":961,"publishedAt":962,"featured":11,"image":11,"development_stage":963,"domain":966,"issuing_body":967,"ai_standard_keywords":968},"py8iqyt9932vvj7704mioyjw","ISO\u002FIEC 15938-6:2020","Part 6: Reference software","\u003Cp>The ISO\u002FIEC 15938 series operates on and generates conformant bitstreams. This document provides a specific implementation that behaves in a conformant manner. NOTE 1 Other implementations that conform to the ISO\u002FIEC 15938 series are possible, which do not necessarily use the algorithms or the programming techniques of the reference software. The software contained in this document is known as eXperimentation Model (XM) and is divided into five categories: a) Binary format for MPEG-7 (BiM). This software converts DDL (XML) based descriptions to binary format and vice versa as explained in Clause 5; b) DDL parser and DDL validation parser. The components of this software module are specified in Clause 6; c) Visual descriptors. This software creates standard visual descriptions from associated (visual) media content as explained in Clause 7.; NOTE 2 The techniques used for extracting descriptors are informative, and the quality and complexity of these extraction tools has not been optimized.; d) Audio descriptors. This software creates standard descriptions from associated (audio) media content as explained in Clause 8.; NOTE 3 The techniques used for extracting descriptors are informative, and the quality and complexity of these extraction tools has not been optimized.; e) Multimedia description schemes. This software modules provide standard descriptions of multimedia description schemes as specified in Clause 9.\u003C\u002Fp>","2020","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F68939.html","2026-01-27T12:43:12.484Z","2026-01-27T12:45:24.304Z","2026-01-27T12:45:24.389Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":964},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":965,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[969,970,976,982,988,994,1000,1006,1012],{"id":898,"documentId":899,"title":900,"createdAt":901,"updatedAt":901,"publishedAt":902},{"id":971,"documentId":972,"title":973,"createdAt":974,"updatedAt":974,"publishedAt":975},220,"rp7ix8swltd4vy7n7ix574vt","Reference software","2026-01-26T23:39:26.977Z","2026-01-26T23:39:27.069Z",{"id":977,"documentId":978,"title":979,"createdAt":980,"updatedAt":980,"publishedAt":981},221,"rqhszy37rg3f6ytq8xuy6v7b","Conformance","2026-01-26T23:39:27.001Z","2026-01-26T23:39:27.096Z",{"id":983,"documentId":984,"title":985,"createdAt":986,"updatedAt":986,"publishedAt":987},222,"lx36zikn5xl4mczm7mq4hvq7","Experimentation Model (XM)","2026-01-26T23:39:27.005Z","2026-01-26T23:39:27.100Z",{"id":989,"documentId":990,"title":991,"createdAt":992,"updatedAt":992,"publishedAt":993},233,"fjbl6dyxh0n5fdrh9fg0svo2","Binary format for MPEG-7 (BIM)","2026-01-26T23:39:27.291Z","2026-01-26T23:39:27.397Z",{"id":995,"documentId":996,"title":997,"createdAt":998,"updatedAt":998,"publishedAt":999},234,"lbckxmrx5ggzrg0oal3u0z7j","DDL parser","2026-01-26T23:39:27.295Z","2026-01-26T23:39:27.401Z",{"id":1001,"documentId":1002,"title":1003,"createdAt":1004,"updatedAt":1004,"publishedAt":1005},235,"ygjxqxmliuu32i0shkm0g4sh","Visual descriptors","2026-01-26T23:39:27.299Z","2026-01-26T23:39:27.405Z",{"id":1007,"documentId":1008,"title":1009,"createdAt":1010,"updatedAt":1010,"publishedAt":1011},236,"k5tkztbjvl2sqvp3c5gjxen9","Audio descriptors","2026-01-26T23:39:27.302Z","2026-01-26T23:39:27.408Z",{"id":1013,"documentId":1014,"title":1015,"createdAt":1016,"updatedAt":1016,"publishedAt":1017},237,"pwzsteeet1tirwpz9nl6ao6t","Multimedia description schemes","2026-01-26T23:39:27.306Z","2026-01-26T23:39:27.412Z",{"id":217,"documentId":1019,"number":1020,"title":656,"part":1021,"abstract":1022,"lastUpdatedYear":886,"linkToOfficial":1023,"createdAt":1024,"updatedAt":1025,"publishedAt":1026,"featured":11,"image":11,"development_stage":1027,"domain":1030,"issuing_body":1031,"ai_standard_keywords":1032},"m6eg31upoouhh7h8vopf6fqn","ISO\u002FIEC 15938-7:2003","Part 7: Conformance testing","\u003Cp>ISO\u002FIEC 15938-7:2003 specifies a metadata system for describing multimedia content. ISO\u002FIEC 15938-7:2003 specifies how tests can be designed to verify whether descriptions and description consuming terminals meet the specifications of parts 1, 2, 3, 4 and 5 of ISO\u002FIEC IEC 15938. In ISO\u002FIEC 15938-7:2003, the creation or extraction of descriptions from multimedia content is not addressed specifically. A system producing descriptions may be said to be an ISO\u002FIEC 15938 compatible description production system if it produces descriptions (binary or textual) that conform to the specifications of parts 1, 2, 3, 4 and 5 of ISO\u002FIEC 15938. The characteristics of descriptions and the terminals consuming descriptions are defined for parts 1, 2, 3, 4 and 5 of ISO\u002FIEC 15938 as follows. Descriptions: the characteristics of a specific description are defined according to syntax and semantics of elements from ISO\u002FIEC 15938 (all parts) that are used in the description.; Guidelines are given on constructing tests to verify conformance of descriptions and terminals. ISO\u002FIEC 15938-7:2003 provides additional guidelines on how to construct test suites for checking conformance of terminals. In addition, some test descriptions are provided. The conformance testing consists of two stages: Systems testing and Description Definition Language (DDL) testing. The Systems conformance testing involves decoding the description stream, which may be in binary or textual access unit form, and checking that after the decoding of each access unit the current description tree maintained by the terminal is DDL conformant. In the case that the input description to the Systems processor is an XML file, the conformance is directly performed by the DDL testing. The DDL conformance testing involves parsing the textual XML description and checking that the description is well-formed and valid according to the schema comprised from parts 1, 2, 3, 4 and 5 of ISO\u002FIEC 15938. The conformance testing of descriptions does not involve checking the semantics of the descriptions.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F35365.html","2026-01-27T12:45:32.301Z","2026-01-27T12:47:45.553Z","2026-01-27T12:47:45.595Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1028},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1029,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1033,1034,1040,1046,1052,1058,1064,1070],{"id":739,"documentId":740,"title":741,"createdAt":742,"updatedAt":742,"publishedAt":743},{"id":1035,"documentId":1036,"title":1037,"createdAt":1038,"updatedAt":1038,"publishedAt":1039},238,"d2rwz2q4c8u37j2f7m8m2zze","Conformance testing","2026-01-26T23:39:27.310Z","2026-01-26T23:39:27.416Z",{"id":1041,"documentId":1042,"title":1043,"createdAt":1044,"updatedAt":1044,"publishedAt":1045},249,"tnajua66ij5rmkyxyfrtcp3x","Description production system","2026-01-26T23:39:27.314Z","2026-01-26T23:39:27.480Z",{"id":1047,"documentId":1048,"title":1049,"createdAt":1050,"updatedAt":1050,"publishedAt":1051},241,"mol8thjyu2kb54glzbtgo10e","Description consuming terminals","2026-01-26T23:39:27.332Z","2026-01-26T23:39:27.468Z",{"id":1053,"documentId":1054,"title":1055,"createdAt":1056,"updatedAt":1056,"publishedAt":1057},239,"v6c1fvem9y8caqn942jiji2e","Systems testing","2026-01-26T23:39:27.318Z","2026-01-26T23:39:27.422Z",{"id":1059,"documentId":1060,"title":1061,"createdAt":1062,"updatedAt":1062,"publishedAt":1063},240,"pw4heqkhfgx7boazh5pidkqe","Description Definition Language (DDL) testing","2026-01-26T23:39:27.321Z","2026-01-26T23:39:27.425Z",{"id":1065,"documentId":1066,"title":1067,"createdAt":1068,"updatedAt":1068,"publishedAt":1069},252,"ni2ht0wrxyliihvh4twawx6x","Test descriptions","2026-01-26T23:39:27.494Z","2026-01-26T23:39:27.582Z",{"id":977,"documentId":978,"title":979,"createdAt":980,"updatedAt":980,"publishedAt":981},{"id":1072,"documentId":1073,"number":1074,"title":656,"part":1075,"abstract":1076,"lastUpdatedYear":1077,"linkToOfficial":1078,"createdAt":1079,"updatedAt":1080,"publishedAt":1081,"featured":11,"image":11,"development_stage":1082,"domain":1085,"issuing_body":1086,"ai_standard_keywords":1087},59,"njpsfijm4j2ccpptcuo67cez","ISO\u002FIEC 15938-9:2005","Part 9: Profiles and levels","\u003Cp>ISO\u002FIEC 15938-9:2005 collects standard profiles and levels for MPEG-7, specified across all parts of ISO\u002FIEC 15938. While all parts are potential candidates for profiling, current profiles concentrate on the Description definition language [ISO\u002FIEC 15938-2], Visual [ISO\u002FIEC 15938-3], Audio [ISO\u002FIEC 15938-4], Multimedia description schemes [ISO\u002FIEC 15938-5], which are based on the namespace versioning defined in Schema definition [ISO\u002FIEC 15938-10]. I\u003C\u002Fp>\u003Cp>SO\u002FIEC 15938-9:2005 provides a set of well-defined definitions and processing rules for description profile and description level, where: \u003Cbr>- A description profile provides a means of selecting and constraining description tools from the MPEG-7 schema, thereby constraining conforming descriptions in their content. A description profile generally limits the use of description tools to subsets of the description tools defined in MPEG-7. The description tools in a description profile support a set of functionalities for a certain class of applications.\u003Cbr>- A description level defines further constraints to limit the complexity of conforming descriptions, for a given description profile. Such constraints may be restrictions on the syntax and\u002For semantics of the selected description tools. For example, a particular element within a data type may be excluded from occurring in descriptions. Description levels may mandate the usage of a data type in descriptions, and may also limit the size of conforming descriptions.\u003C\u002Fp>\u003Cp>Currently, ISO\u002FIEC 15938-9:2005 standardizes the following profiles: \u003Cbr>- Simple Metadata Profile (SMP) - describes simple metadata tagging for single instances of multimedia clips. This profile can be used in the areas such as music, images and mobile applications, just to name a few.\u003Cbr>- User Description Profile (UDP) - describes the personal preferences and usage patterns of users of multimedia content. Descriptions of users' preferences enable automatic discovery, selection and recommendation or recording of multimedia content. Preferences can be automatically inferred from the user's prior viewing and listening habits, which can in turn be derived from a usage history. The goal is to support improved usability of a variety of multimedia devices through personalization: personalized multimedia services offered to the consumer, personalized multimedia content discovery, filtering and selection, and personalized consumption of multimedia content.\u003Cbr>- Core Description Profile (CDP) - describes general multimedia content such as images, videos, audio, and collections. Furthermore, content collections spanning various media types can also be described. As such, this profile provides a core set of tools that can be used to describe common multimedia content for a broad range of applications, such as general multimedia creation, consumption and distribution, broadcast television programming and educational courseware, just to name a few.\u003C\u002Fp>","2005","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F40392.html","2026-01-27T12:47:52.720Z","2026-01-27T12:50:21.544Z","2026-01-27T12:50:21.591Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1083},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1084,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1088,1089,1095,1101,1107,1113,1114,1120,1126],{"id":677,"documentId":678,"title":679,"createdAt":680,"updatedAt":680,"publishedAt":681},{"id":1090,"documentId":1091,"title":1092,"createdAt":1093,"updatedAt":1093,"publishedAt":1094},253,"d4i0jvj4p8yw577ipucpj9y7","Standard profiles","2026-01-26T23:39:27.498Z","2026-01-26T23:39:27.585Z",{"id":1096,"documentId":1097,"title":1098,"createdAt":1099,"updatedAt":1099,"publishedAt":1100},254,"e6p6ia9w0cg6xs5095pvz796","Description profile","2026-01-26T23:39:27.502Z","2026-01-26T23:39:27.588Z",{"id":1102,"documentId":1103,"title":1104,"createdAt":1105,"updatedAt":1105,"publishedAt":1106},255,"un9x1xkpjktgk3lmhoh897k0","Description level","2026-01-26T23:39:27.505Z","2026-01-26T23:39:27.592Z",{"id":1108,"documentId":1109,"title":1110,"createdAt":1111,"updatedAt":1111,"publishedAt":1112},256,"ufaycl42ho3qy9zo61qt7pig","Constraints","2026-01-26T23:39:27.509Z","2026-01-26T23:39:27.595Z",{"id":806,"documentId":807,"title":808,"createdAt":809,"updatedAt":809,"publishedAt":810},{"id":1115,"documentId":1116,"title":1117,"createdAt":1118,"updatedAt":1118,"publishedAt":1119},257,"wadx7fqmxuta2rlf2mmqdvpi","Simple Metadata Profile (SMP)","2026-01-26T23:39:27.525Z","2026-01-26T23:39:27.601Z",{"id":1121,"documentId":1122,"title":1123,"createdAt":1124,"updatedAt":1124,"publishedAt":1125},258,"xfu515aeql767pvjpnrfh2u8","User Description Profile (UDP)","2026-01-26T23:39:27.528Z","2026-01-26T23:39:27.605Z",{"id":1127,"documentId":1128,"title":1129,"createdAt":1130,"updatedAt":1130,"publishedAt":1131},263,"srj9e4mijudue2skfqmuqiko","Core Description Profile (CDP)","2026-01-26T23:39:27.565Z","2026-01-26T23:39:27.646Z",{"id":1133,"documentId":1134,"number":1135,"title":656,"part":1136,"abstract":1137,"lastUpdatedYear":1077,"linkToOfficial":1138,"createdAt":1139,"updatedAt":1140,"publishedAt":1141,"featured":11,"image":11,"development_stage":1142,"domain":1145,"issuing_body":1146,"ai_standard_keywords":1147},61,"r91mnyyomuj1r4r1mdpryziu","ISO\u002FIEC 15938-10:2005","Part 10: Schema definition","\u003Cp>ISO\u002FIEC 15938-10:2005 specifies a metadata system for describing multimedia content. It specifies the schema definition across all parts of ISO\u002FIEC 15938. ISO\u002FIEC 15938-10:2005 collects together the description tools specified in ISO\u002FIEC 15938, assigns a namespace designator, and specifies the resulting syntax description in a single schema using description definition language from ISO\u002FIEC 15938-2.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F40527.html","2026-01-27T12:50:30.412Z","2026-01-27T12:52:16.504Z","2026-01-27T12:52:16.622Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1143},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1144,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1148,1149,1150,1156,1157,1163,1169],{"id":739,"documentId":740,"title":741,"createdAt":742,"updatedAt":742,"publishedAt":743},{"id":745,"documentId":746,"title":747,"createdAt":748,"updatedAt":748,"publishedAt":749},{"id":1151,"documentId":1152,"title":1153,"createdAt":1154,"updatedAt":1154,"publishedAt":1155},269,"zmf09du1x78173b4e49n10zp","Schema definition","2026-01-26T23:39:27.568Z","2026-01-26T23:39:27.699Z",{"id":806,"documentId":807,"title":808,"createdAt":809,"updatedAt":809,"publishedAt":810},{"id":1158,"documentId":1159,"title":1160,"createdAt":1161,"updatedAt":1161,"publishedAt":1162},270,"g1ja1ttkh2a8uiyq59yex39n","Namespace designator","2026-01-26T23:39:27.652Z","2026-01-26T23:39:27.731Z",{"id":1164,"documentId":1165,"title":1166,"createdAt":1167,"updatedAt":1167,"publishedAt":1168},271,"qoxa5s30ywzspp9r6aea6p3c","Syntax description","2026-01-26T23:39:27.671Z","2026-01-26T23:39:27.752Z",{"id":1170,"documentId":1171,"title":1172,"createdAt":1173,"updatedAt":1173,"publishedAt":1174},272,"f6nzli53xbclewg0iln6c1c1","Description definition language","2026-01-26T23:39:27.674Z","2026-01-26T23:39:27.756Z",{"id":1176,"documentId":1177,"number":1178,"title":656,"part":1179,"abstract":1180,"lastUpdatedYear":1181,"linkToOfficial":1182,"createdAt":1183,"updatedAt":1184,"publishedAt":1185,"featured":11,"image":11,"development_stage":1186,"domain":1189,"issuing_body":1190,"ai_standard_keywords":1191},63,"fenzeg3gj3vd1gop9ju4eyfg","ISO\u002FIEC 15938-12:2012","Part 12: Query format","\u003Cp>ISO\u002FIEC 15938-12:2012 describes the query format tools which may be used independently or in combination with other parts of ISO\u002FIEC 15938. Each query format tool is described in two normative sections: a) Syntax: Normative specification of the query and management format.; b) Semantic: Normative definition of the semantics of all the components of the corresponding query format specification.; In some instances the query format level tool is also described using either one or two informative sections: a) Examples:Optionally, an informative section dealing with examples is appended.; b) Definitions:Optionally, an informative section dealing with definitions is appended.\u003C\u002Fp>\u003Cp>The query format provides a standardized interface for multimedia content information retrieval systems (e.g. MPEG-7 databases) in three aspects which are input query format, output query format, and query managements. The input query format specifies the interface through which the users can describe their search criteria with a set of precise input parameters in addition to a set of preferred output parameters to depict the return result sets. The output query format specifies the interface format for the result set. The query management provides means for selecting services (e.g. MPEG-7 database) or aggregated services (e.g. service provider that administers a set of different services) based on service properties (e.g. supported query format).\u003C\u002Fp>","2012","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F61195.html","2026-01-27T12:52:32.301Z","2026-01-27T12:54:25.005Z","2026-01-27T12:54:25.057Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1187},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1188,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1192,1198,1204,1210,1216,1222,1228],{"id":1193,"documentId":1194,"title":1195,"createdAt":1196,"updatedAt":1196,"publishedAt":1197},273,"woskfof1tei9wbovkf2pnrh4","Multimedia content information retrieval systems","2026-01-26T23:39:27.677Z","2026-01-26T23:39:27.764Z",{"id":1199,"documentId":1200,"title":1201,"createdAt":1202,"updatedAt":1202,"publishedAt":1203},274,"k32079ashmt29a20qj9x4c99","Query format tools","2026-01-26T23:39:27.685Z","2026-01-26T23:39:27.777Z",{"id":1205,"documentId":1206,"title":1207,"createdAt":1208,"updatedAt":1208,"publishedAt":1209},275,"mk9pjf4p3og53ebzs0ey397q","Input query format","2026-01-26T23:39:27.689Z","2026-01-26T23:39:27.781Z",{"id":1211,"documentId":1212,"title":1213,"createdAt":1214,"updatedAt":1214,"publishedAt":1215},276,"rnvuz0sglhesv08vccwfdiuc","Output query format","2026-01-26T23:39:27.697Z","2026-01-26T23:39:27.792Z",{"id":1217,"documentId":1218,"title":1219,"createdAt":1220,"updatedAt":1220,"publishedAt":1221},286,"lfiu7a82nj5yo8kc8fucqncm","Query management","2026-01-26T23:39:27.714Z","2026-01-26T23:39:27.846Z",{"id":1223,"documentId":1224,"title":1225,"createdAt":1226,"updatedAt":1226,"publishedAt":1227},281,"to17b5qgiyz06nslhqvqwf5n","MPEG-7 databases","2026-01-26T23:39:27.746Z","2026-01-26T23:39:27.834Z",{"id":1229,"documentId":1230,"title":1231,"createdAt":1232,"updatedAt":1232,"publishedAt":1233},288,"pgk40vvjesondd5v9ie90tpf","Service properties","2026-01-26T23:39:27.833Z","2026-01-26T23:39:27.921Z",{"id":1235,"documentId":1236,"number":1237,"title":656,"part":1238,"abstract":1239,"lastUpdatedYear":1240,"linkToOfficial":1241,"createdAt":1242,"updatedAt":1243,"publishedAt":1244,"featured":11,"image":11,"development_stage":1245,"domain":1248,"issuing_body":1249,"ai_standard_keywords":1250},65,"o1kjkepbmjv5kv3gdy48yhwx","ISO\u002FIEC 15938-13:2015","Part 13: Compact descriptors for visual search","\u003Cp>1.1 Organization of the document; ISO\u002FIEC 15938-13:2015 is as follows. Clauses 2-3 specify the terms, abbreviations, symbols and conventions used in the document. Clause 4 specifies the binary representation syntax and descriptor component semantics for a CDVS image descriptor. Clause 5 specifies the extraction and encoding process for a CDVS image descriptor. Annexes A-J specify information relevant to the encoding process of Clause 5. Annex K contains an informative description of the decoding process of a CDVS image descriptor.\u003C\u002Fp>\u003Cp>1.2 Overview of compact descriptors for visual search; This part of the MPEG-7 standard specifies an image description tool designed to enable efficient and interoperable visual search applications, allowing visual content matching in images. Visual content matching includes matching of views of objects, landmarks, and printed documents, while being robust to partial occlusions as well as changes in viewpoint, camera parameters, and lighting conditions.\u003C\u002Fp>","2015","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F65393.html","2026-01-27T12:54:53.199Z","2026-01-27T12:56:38.370Z","2026-01-27T12:56:38.422Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1246},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1247,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1251,1252,1258,1264,1270,1276,1282],{"id":677,"documentId":678,"title":679,"createdAt":680,"updatedAt":680,"publishedAt":681},{"id":1253,"documentId":1254,"title":1255,"createdAt":1256,"updatedAt":1256,"publishedAt":1257},287,"dvbnv75ajb2qhfphburj4sof","Compact descriptors for visual search (CDVS)","2026-01-26T23:39:27.853Z","2026-01-26T23:39:27.936Z",{"id":1259,"documentId":1260,"title":1261,"createdAt":1262,"updatedAt":1262,"publishedAt":1263},291,"d6jwdhntr1dqsva3f7zix02z","Image descriptor","2026-01-26T23:39:27.856Z","2026-01-26T23:39:27.956Z",{"id":1265,"documentId":1266,"title":1267,"createdAt":1268,"updatedAt":1268,"publishedAt":1269},292,"mp8ylsfukt4dkwnr04nc4s2v","Visual search applications","2026-01-26T23:39:27.859Z","2026-01-26T23:39:27.959Z",{"id":1271,"documentId":1272,"title":1273,"createdAt":1274,"updatedAt":1274,"publishedAt":1275},289,"v96y1os1461hkvbqwn49nvk3","Visual content matching","2026-01-26T23:39:27.868Z","2026-01-26T23:39:27.947Z",{"id":1277,"documentId":1278,"title":1279,"createdAt":1280,"updatedAt":1280,"publishedAt":1281},290,"tck2qxvsx8uiq4i8rteo3q7w","Encoding process","2026-01-26T23:39:27.872Z","2026-01-26T23:39:27.950Z",{"id":1283,"documentId":1284,"title":1285,"createdAt":1286,"updatedAt":1286,"publishedAt":1287},293,"zgcs4rp6xv1i6v0e0mhmrvty","Decoding process","2026-01-26T23:39:27.881Z","2026-01-26T23:39:27.969Z",{"id":1289,"documentId":1290,"number":1291,"title":656,"part":1292,"abstract":1293,"lastUpdatedYear":329,"linkToOfficial":1294,"createdAt":1295,"updatedAt":1296,"publishedAt":1297,"featured":11,"image":11,"development_stage":1298,"domain":1301,"issuing_body":1302,"ai_standard_keywords":1303},67,"g9shvpnpoby7lr060nmj65fj","ISO\u002FIEC 15938-14:2018","Part 14: Reference software, conformance and usage guidelines for compact descriptors for visual search","\u003Cp>ISO\u002FIEC 15938-14:2018 provides the reference software, specifies the conformance testing, and gives usage guidelines for ISO\u002FIEC 15938‑13.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F69664.html","2026-01-27T12:56:44.604Z","2026-01-27T12:58:37.331Z","2026-01-27T12:58:37.362Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1299},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1300,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1304,1310,1311,1312,1318],{"id":1305,"documentId":1306,"title":1307,"createdAt":1308,"updatedAt":1308,"publishedAt":1309},295,"plabr0ns0urvmzv44iqcvkdd","Compact descriptors for visual search","2026-01-26T23:39:27.897Z","2026-01-26T23:39:27.983Z",{"id":971,"documentId":972,"title":973,"createdAt":974,"updatedAt":974,"publishedAt":975},{"id":1035,"documentId":1036,"title":1037,"createdAt":1038,"updatedAt":1038,"publishedAt":1039},{"id":1313,"documentId":1314,"title":1315,"createdAt":1316,"updatedAt":1316,"publishedAt":1317},305,"ovwv8tc5sh5dkuz9kmphbedk","Usage guidelines","2026-01-26T23:39:28.008Z","2026-01-26T23:39:28.097Z",{"id":1319,"documentId":1320,"title":1321,"createdAt":1322,"updatedAt":1322,"publishedAt":1323},314,"kqd2cbqbzje4eoamub9f9cuw","ISO\u002FIEC 15938-13","2026-01-26T23:39:28.036Z","2026-01-26T23:39:28.183Z",{"id":14,"documentId":1325,"number":1326,"title":656,"part":1327,"abstract":1328,"lastUpdatedYear":398,"linkToOfficial":1329,"createdAt":1330,"updatedAt":1331,"publishedAt":1332,"featured":11,"image":11,"development_stage":1333,"domain":1336,"issuing_body":1337,"ai_standard_keywords":1338},"aa4sc3wlwjtom7mw0v934erl","ISO\u002FIEC 15938-15:2019","Part 15: Compact descriptors for video analysis","\u003Cp>This document addresses descriptor technology for search and retrieval applications, i.e. for visual content matching in video. Visual content matching includes matching of views of large and small objects and scenes, with robustness to partial occlusions as well as changes in vantage point, camera parameters and lighting conditions. The objects of interest comprise planar or non-planar, rigid or partially rigid, textured or partially textured objects, but exclude the identification of people and faces. The databases can be large, for example broadcast archives or videos available on the internet. Such applications thus require video descriptors that enable matching with smaller descriptor sizes and shorter runtimes as compared to application enabled by single-frame (still image) descriptors (e.g. CVDS, ISO\u002FIEC 15938-13) in the video domain. Compact descriptors for video analysis for search and retrieval applications: — enable design of interoperable object instance search applications; — minimize the size of video descriptors; ensure high matching performances of objects (in terms of accuracy and complexity); enable efficient implementation of those functionalities on professional or embedded systems.; This document provides a complementary tool to the suite of existing standards, such as ISO\u002FIEC 15938-13.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F75399.html","2026-01-27T12:58:47.580Z","2026-01-27T13:00:29.457Z","2026-01-27T13:00:29.485Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1334},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1335,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1339,1345,1346,1352,1358,1364,1370],{"id":1340,"documentId":1341,"title":1342,"createdAt":1343,"updatedAt":1343,"publishedAt":1344},306,"tpttm8tzkph9u53yiq1hcgbv","Descriptor technology","2026-01-26T23:39:28.041Z","2026-01-26T23:39:28.125Z",{"id":1271,"documentId":1272,"title":1273,"createdAt":1274,"updatedAt":1274,"publishedAt":1275},{"id":1347,"documentId":1348,"title":1349,"createdAt":1350,"updatedAt":1350,"publishedAt":1351},307,"viha50umja8v39p2s36fedg5","Video analysis","2026-01-26T23:39:28.050Z","2026-01-26T23:39:28.136Z",{"id":1353,"documentId":1354,"title":1355,"createdAt":1356,"updatedAt":1356,"publishedAt":1357},308,"o9ntdcgw05rv1lefcclc31u7","Compact descriptors","2026-01-26T23:39:28.053Z","2026-01-26T23:39:28.140Z",{"id":1359,"documentId":1360,"title":1361,"createdAt":1362,"updatedAt":1362,"publishedAt":1363},309,"jd9zwe68aw21k6kte6o52vj9","Video descriptors","2026-01-26T23:39:28.060Z","2026-01-26T23:39:28.147Z",{"id":1365,"documentId":1366,"title":1367,"createdAt":1368,"updatedAt":1368,"publishedAt":1369},310,"m1nkwtp004mjhtg4xqyixwd0","Search and retrieval applications","2026-01-26T23:39:28.063Z","2026-01-26T23:39:28.155Z",{"id":1371,"documentId":1372,"title":1373,"createdAt":1374,"updatedAt":1374,"publishedAt":1375},318,"rpqtozff2gqxuauywr7ma731","Object instance search","2026-01-26T23:39:28.068Z","2026-01-26T23:39:28.226Z",{"id":287,"documentId":1377,"number":1378,"title":656,"part":1379,"abstract":1380,"lastUpdatedYear":60,"linkToOfficial":1381,"createdAt":1382,"updatedAt":1383,"publishedAt":1384,"featured":11,"image":11,"development_stage":1385,"domain":1388,"issuing_body":1389,"ai_standard_keywords":1390},"e8iunjri6xnyhw786lt372jz","ISO\u002FIEC 15938-16:2021","Part 16: Conformance and reference software for compact descriptors for video analysis","\u003Cp>This document specifies the assessment of conformance to ISO\u002FIEC 15938-15 as well as the reference software.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F77232.html","2026-01-27T13:01:00.908Z","2026-01-27T13:02:32.753Z","2026-01-27T13:02:32.792Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1386},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1387,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1391,1397,1403,1404],{"id":1392,"documentId":1393,"title":1394,"createdAt":1395,"updatedAt":1395,"publishedAt":1396},311,"toj7199x41q5yda04kdz765q","Compact descriptors for video analysis","2026-01-26T23:39:28.077Z","2026-01-26T23:39:28.167Z",{"id":1398,"documentId":1399,"title":1400,"createdAt":1401,"updatedAt":1401,"publishedAt":1402},313,"kq365nuhaqciwmdwqpz1euor","Conformance assessment","2026-01-26T23:39:28.092Z","2026-01-26T23:39:28.196Z",{"id":971,"documentId":972,"title":973,"createdAt":974,"updatedAt":974,"publishedAt":975},{"id":1405,"documentId":1406,"title":1407,"createdAt":1408,"updatedAt":1408,"publishedAt":1409},319,"kya6arzqz5rw61znm41ohjz8","ISO\u002FIEC 15938-15","2026-01-26T23:39:28.215Z","2026-01-26T23:39:28.275Z",{"id":299,"documentId":1411,"number":1412,"title":656,"part":1413,"abstract":1414,"lastUpdatedYear":1415,"linkToOfficial":1416,"createdAt":1417,"updatedAt":1418,"publishedAt":1419,"featured":11,"image":11,"development_stage":1420,"domain":1423,"issuing_body":1424,"ai_standard_keywords":1425},"biqih8aluswa3hivwltuzptj","ISO\u002FIEC 15938-17:2024","Part 17: Compression of neural networks for multimedia content description and analysis","\u003Cp>This document specifies Neural Network Coding (NNC) as a compressed representation of the parameters\u002Fweights of a trained neural network and a decoding process for the compressed representation, complementing the description of the network topology in existing (exchange) formats for neural networks. It establishes a toolbox of compression methods, specifying (where applicable) the resulting elements of the compressed bitstream. Most of these tools can be applied to the compression of entire neural networks, and some of them can also be applied to the compression of differential updates of neural networks with respect to a base network. Such differential updates are for example useful when models are redistributed after fine-tuning or transfer learning, or when providing versions of a neural network with different compression ratios. This document does not specify a complete protocol for the transmission of neural networks, but focuses on compression of network parameters. Only the syntax format, semantics, associated decoding process requirements, parameter sparsification, parameter transformation methods, parameter quantization, entropy coding method and integration\u002Fsignalling within existing exchange formats are specified, while other matters such as pre-processing, system signalling and multiplexing, data loss recovery and post-processing are considered to be outside the scope of this document. Additionally, the internal processing steps performed within a decoder are also considered to be outside the scope of this document; only the externally observable output behaviour is required to conform to the specifications of this document.\u003C\u002Fp>","2024","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F85545.html","2026-01-27T13:02:39.233Z","2026-01-27T13:04:25.023Z","2026-01-27T13:04:25.073Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1421},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1422,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1426,1432,1438,1444,1450,1451,1457,1463],{"id":1427,"documentId":1428,"title":1429,"createdAt":1430,"updatedAt":1430,"publishedAt":1431},320,"k861syat0aiogogvdg6aja54","Compression of neural networks","2026-01-26T23:39:28.241Z","2026-01-26T23:39:28.306Z",{"id":1433,"documentId":1434,"title":1435,"createdAt":1436,"updatedAt":1436,"publishedAt":1437},321,"edo77awxu8oj2oc6yrr6p5gd","Neural Network Coding (NNC)","2026-01-26T23:39:28.251Z","2026-01-26T23:39:28.313Z",{"id":1439,"documentId":1440,"title":1441,"createdAt":1442,"updatedAt":1442,"publishedAt":1443},322,"jrbytgpf7klz4l3p0xfp0dwv","Network parameters","2026-01-26T23:39:28.254Z","2026-01-26T23:39:28.316Z",{"id":1445,"documentId":1446,"title":1447,"createdAt":1448,"updatedAt":1448,"publishedAt":1449},333,"v908mmsftrtaukyppb2833ks","Weights","2026-01-26T23:39:28.589Z","2026-01-26T23:39:28.697Z",{"id":1283,"documentId":1284,"title":1285,"createdAt":1286,"updatedAt":1286,"publishedAt":1287},{"id":1452,"documentId":1453,"title":1454,"createdAt":1455,"updatedAt":1455,"publishedAt":1456},334,"kc12ka1038fcvq2kuwe567wc","Compression methods","2026-01-26T23:39:28.593Z","2026-01-26T23:39:28.700Z",{"id":1458,"documentId":1459,"title":1460,"createdAt":1461,"updatedAt":1461,"publishedAt":1462},335,"qu0t8rjdoe20nssv4bw6eft1","Differential updates","2026-01-26T23:39:28.596Z","2026-01-26T23:39:28.703Z",{"id":1464,"documentId":1465,"title":1466,"createdAt":1467,"updatedAt":1467,"publishedAt":1468},336,"y43lyd5yywjjxikf3hzdigqy","Entropy coding","2026-01-26T23:39:28.599Z","2026-01-26T23:39:28.706Z",{"id":306,"documentId":1470,"number":1471,"title":656,"part":1472,"abstract":1473,"lastUpdatedYear":524,"linkToOfficial":1474,"createdAt":1475,"updatedAt":1476,"publishedAt":1477,"featured":11,"image":11,"development_stage":1478,"domain":1481,"issuing_body":1482,"ai_standard_keywords":1483},"x1p0wzvll356cu980b60o8bp","ISO\u002FIEC 15938-18:2025","Part 18: Conformance and reference software for compression of neural networks","\u003Cp>This document specifies conformance testing procedures for implementations of ISO\u002FIEC 15938-17 and provides conformance bitstreams. It also provides the reference software for ISO\u002FIEC 15938-17 which is an integral part of this document.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F87634.html","2026-01-27T13:04:46.721Z","2026-01-27T13:06:17.448Z","2026-01-27T13:06:17.504Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1479},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1480,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1484,1485,1491,1497,1498],{"id":1427,"documentId":1428,"title":1429,"createdAt":1430,"updatedAt":1430,"publishedAt":1431},{"id":1486,"documentId":1487,"title":1488,"createdAt":1489,"updatedAt":1489,"publishedAt":1490},340,"ykbn9i3f9x3chvpi8ktd4d0u","Conformance testing procedures","2026-01-26T23:39:28.617Z","2026-01-26T23:39:28.729Z",{"id":1492,"documentId":1493,"title":1494,"createdAt":1495,"updatedAt":1495,"publishedAt":1496},341,"aygnm2shcryf412fi260hamd","Conformance bitstreams","2026-01-26T23:39:28.620Z","2026-01-26T23:39:28.746Z",{"id":971,"documentId":972,"title":973,"createdAt":974,"updatedAt":974,"publishedAt":975},{"id":1499,"documentId":1500,"title":1501,"createdAt":1502,"updatedAt":1502,"publishedAt":1503},339,"jujbc7kdl38r6v1y1ox0r5e0","ISO\u002FIEC 15938-17","2026-01-26T23:39:28.613Z","2026-01-26T23:39:28.727Z",{"id":1505,"documentId":1506,"number":1507,"title":1508,"part":1509,"abstract":1510,"lastUpdatedYear":1415,"linkToOfficial":1511,"createdAt":1512,"updatedAt":1513,"publishedAt":1514,"featured":11,"image":11,"development_stage":1515,"domain":1518,"issuing_body":1519,"ai_standard_keywords":1520},79,"ybtb93f486rqymv538g8vs00","ISO\u002FIEC 15067-3-51:2024","Information technology — Home Electronic System (HES) application model","Part 3-51: Framework of a narrow AI engine for a premises energy management system using energy management agents","\u003Cp>This document specifies a Narrow AI Engine Framework including requirements for the Energy Management Agent (EMA) specified in ISO\u002FIEC 15067-3. This standard includes specifications for an AI infrastructure to be embedded in an EMA serving a single structure (home or building) or community housing such as an apartment complex. The Narrow AI Engine specified in this standard for EMAs enables demand response functionality to be located in each EMA instead of an external energy management system. Thus, energy management can be adapted to local and customer needs. The Narrow AI Engine includes operational principles such as prediction, decision–making, and control. This standard builds upon the EMA functions of ISO\u002FIEC 15067-3 and ISO\u002FIEC 15067-3-3. The AI functions specified in this standard support complex decisions about energy management for devices attached to home and buildings networks. These AI specifications enable the EMA to allocate power from public sources (including conventional and nonconventional sources) and local sources (wind, solar, and storage) according to price, availability, appliance and electric vehicle demands, customer preferences, and the customer’s budget.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F89965.html","2026-01-27T13:56:38.141Z","2026-01-27T13:58:47.304Z","2026-01-27T13:58:47.366Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1516},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1517,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":179,"documentId":180,"title":181,"createdAt":182,"updatedAt":182,"publishedAt":183},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1521,1527,1533,1539,1545,1551,1557,1563],{"id":1522,"documentId":1523,"title":1524,"createdAt":1525,"updatedAt":1525,"publishedAt":1526},357,"ugaog6h6lsm1okthhgby9g6o","Narrow AI Engine Framework","2026-01-26T23:39:28.821Z","2026-01-26T23:39:28.904Z",{"id":1528,"documentId":1529,"title":1530,"createdAt":1531,"updatedAt":1531,"publishedAt":1532},358,"oa87t2fon5fulh3z7vqlvxhe","Energy Management Agent (EMA)","2026-01-26T23:39:28.827Z","2026-01-26T23:39:28.910Z",{"id":1534,"documentId":1535,"title":1536,"createdAt":1537,"updatedAt":1537,"publishedAt":1538},359,"tgoxthma2m355xhd8phvpi6j","Premises energy management system","2026-01-26T23:39:28.830Z","2026-01-26T23:39:28.912Z",{"id":1540,"documentId":1541,"title":1542,"createdAt":1543,"updatedAt":1543,"publishedAt":1544},370,"ikml98zy7jghjtt55rwhc2gb","Demand response","2026-01-26T23:39:28.845Z","2026-01-26T23:39:28.984Z",{"id":1546,"documentId":1547,"title":1548,"createdAt":1549,"updatedAt":1549,"publishedAt":1550},362,"k0l9cvsdrfulpr52nuzbji9j","AI infrastructure","2026-01-26T23:39:28.861Z","2026-01-26T23:39:28.938Z",{"id":1552,"documentId":1553,"title":1554,"createdAt":1555,"updatedAt":1555,"publishedAt":1556},371,"j1zbr1wc1x7cg2gpcj6sygtz","Prediction","2026-01-26T23:39:28.957Z","2026-01-26T23:39:29.048Z",{"id":1558,"documentId":1559,"title":1560,"createdAt":1561,"updatedAt":1561,"publishedAt":1562},372,"mc2lno025i5thb6e785k2ejv","Decision-making","2026-01-26T23:39:28.961Z","2026-01-26T23:39:29.051Z",{"id":1564,"documentId":1565,"title":1566,"createdAt":1567,"updatedAt":1567,"publishedAt":1568},374,"i67rocqf92mrb5a5bxmufon2","Control","2026-01-26T23:39:28.972Z","2026-01-26T23:39:29.059Z",{"id":1570,"documentId":1571,"number":1572,"title":1508,"part":1573,"abstract":1574,"lastUpdatedYear":1415,"linkToOfficial":1575,"createdAt":1576,"updatedAt":1577,"publishedAt":1578,"featured":11,"image":11,"development_stage":1579,"domain":1582,"issuing_body":1583,"ai_standard_keywords":1584},80,"icg45kmczgjff05x8lqd3uxa","ISO\u002FIEC 15067-3:2024","Part 3: Model of an energy management system for HES","\u003Cp>This document focuses on a model of a system in homes and buildings that can manage energy consumption and generation of electricity by devices on premises dynamically in response to electricity availability from: \u003Cbr>- sources within the home or building such as solar panels, wind turbines, or storage (stationary or mobile), \u003Cbr>- neighbourhood microgrids, \u003Cbr>- transactive energy, \u003Cbr>- energy aggregators, and \u003Cbr>- public utilities.\u003C\u002Fp>\u003Cp>This document specifies a model including a framework and methods for energy management consisting of interconnected elements that can be configured to support various methods for a Home Electronic System (HES) energy management system. The methods specified are intended to be generic and representative of a wide range of situations. This document applies to the customer grid-edge portion of the electricity grid (within a home or building) and applies even if the consumer has sufficient local power generation to operate without connecting to a public utility.\u003C\u002Fp>\u003Cp>This document includes an energy management model that balances power supplied from internal and external sources with demand from appliances and electric vehicle chargers. The model offers flexibility for locating the energy management equipment in a stand-alone product, embedded in consumer electronics, or hosted in a gateway. This gateway can be a generic communications interface between a home network and an external network, an energy management gateway designed for handling energy-related data, or the HES gateway specified in the ISO\u002FIEC 15045 series.\u003C\u002Fp>\u003Cp>This model specifies a local controller that achieves the allocation of power in accordance with available supplies, consumer preferences for appliance operation, and power requirements of these appliances within constraints set by the consumer. Such constraints are typically financial (a budget for electricity) but can also include goals such as using green sources and minimizing their impact on climate change. This controller is called the energy management agent (EMA) since it acts as an agent for the consumer. This model accommodates an EMA with technology of artificial intelligence to facilitate energy management.\u003C\u002Fp>\u003Cp>This second edition cancels and replaces the first edition published in 2012. This edition constitutes a technical revision. This edition includes the following significant technical changes with respect to the previous edition: \u003Cbr>a) This edition revises ISO\u002FIEC 15067-3:2012 by expanding beyond demand response to include a balance between multiple sources of power and appliance demands for this power.\u003Cbr>b) This edition specifies a system framework that addresses the need for user-centric energy management by providing control options for consumers.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F90311.html","2026-01-27T13:53:51.264Z","2026-01-27T13:58:57.823Z","2026-01-27T13:58:57.862Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1580},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1581,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":179,"documentId":180,"title":181,"createdAt":182,"updatedAt":182,"publishedAt":183},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1585,1591,1597,1603,1609,1615,1621,1627],{"id":1586,"documentId":1587,"title":1588,"createdAt":1589,"updatedAt":1589,"publishedAt":1590},337,"s7i5304olw9tj1g3hd51abyr","Home Electronic System (HES)","2026-01-26T23:39:28.603Z","2026-01-26T23:39:28.712Z",{"id":1592,"documentId":1593,"title":1594,"createdAt":1595,"updatedAt":1595,"publishedAt":1596},338,"b2r0l2xgzr86abbtgncz8keb","Energy management system (EMS)","2026-01-26T23:39:28.606Z","2026-01-26T23:39:28.716Z",{"id":1598,"documentId":1599,"title":1600,"createdAt":1601,"updatedAt":1601,"publishedAt":1602},344,"sssn4hicetmt6r9tvrk9xluo","Electricity consumption","2026-01-26T23:39:28.647Z","2026-01-26T23:39:28.767Z",{"id":1604,"documentId":1605,"title":1606,"createdAt":1607,"updatedAt":1607,"publishedAt":1608},353,"ao0vksef47vrovc02l5nsss4","Electricity generation","2026-01-26T23:39:28.793Z","2026-01-26T23:39:28.867Z",{"id":1610,"documentId":1611,"title":1612,"createdAt":1613,"updatedAt":1613,"publishedAt":1614},354,"k4672si3a0vuzy95f7ragqmt","Energy management agent (EMA)","2026-01-26T23:39:28.796Z","2026-01-26T23:39:28.869Z",{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},355,"i9fayci3ok5kmgosxvwqnfxf","Artificial intelligence","2026-01-26T23:39:28.803Z","2026-01-26T23:39:28.878Z",{"id":1622,"documentId":1623,"title":1624,"createdAt":1625,"updatedAt":1625,"publishedAt":1626},356,"y8ni1db956y6hx3vz19dfii0","Smart grid","2026-01-26T23:39:28.810Z","2026-01-26T23:39:28.892Z",{"id":1628,"documentId":1629,"title":1630,"createdAt":1631,"updatedAt":1631,"publishedAt":1632},367,"uttwez0znym71t3noae3atom","Consumer preferences","2026-01-26T23:39:28.833Z","2026-01-26T23:39:28.967Z",{"id":1634,"documentId":1635,"number":1636,"title":1637,"part":1638,"abstract":1639,"lastUpdatedYear":1415,"linkToOfficial":1640,"createdAt":1641,"updatedAt":1642,"publishedAt":1643,"featured":11,"image":11,"development_stage":1644,"domain":1647,"issuing_body":1648,"ai_standard_keywords":1649},82,"aq4mrzycw5svj8n6ddtlwzok","ISO\u002FIEC 19795-10:2024","Information technology — Biometric performance testing and reporting","Part 10: Quantifying biometric system performance variation across demographic groups","\u003Cp>This document establishes requirements for estimating and reporting on performance variations observed when cohorts belonging to different demographic groups engage with biometric enrolment and recognition systems. In this context, performance refers to failure-to-enrol rate, failure-to-acquire rate, shifts in comparison score, recognition error rates, and aspects of response and processing time (throughput).\u003C\u002Fp>\u003Cp>This document is applicable to the following: \u003Cbr>- demographic group membership; \u003Cbr>- using phenotypic measures; \u003Cbr>- reporting on tests; \u003Cbr>- stating statistical uncertainty estimates; \u003Cbr>- operational thresholds settings; \u003Cbr>- equitability; \u003Cbr>- procurement agency activities.\u003C\u002Fp>\u003Cp>This document also provides terms and definitions to be used when reporting performance variation across demographic groups.\u003C\u002Fp>\u003Cp>This document is applicable to: \u003Cbr>- technology evaluations of algorithms, subsystems and systems; \u003Cbr>- scenario evaluations of systems; \u003Cbr>- operational evaluations of fielded systems.\u003C\u002Fp>\u003Cp>Application of this document does not require detailed knowledge of a system’s algorithms but it does require specific knowledge of the demographic characteristics for the population of interest.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81223.html","2026-01-27T13:59:14.335Z","2026-01-27T14:01:13.275Z","2026-01-27T14:01:13.323Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1645},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1646,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":66,"documentId":533,"title":534,"createdAt":535,"updatedAt":536,"publishedAt":537},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1650,1656,1662,1668,1674,1680,1686,1692],{"id":1651,"documentId":1652,"title":1653,"createdAt":1654,"updatedAt":1654,"publishedAt":1655},375,"gy2ms7npvcn2k64i7ud7u9uv","Biometric performance testing","2026-01-26T23:39:28.982Z","2026-01-26T23:39:29.068Z",{"id":1657,"documentId":1658,"title":1659,"createdAt":1660,"updatedAt":1660,"publishedAt":1661},376,"e4ko9ja3fxzoct3hmkjpatkz","Performance variation","2026-01-26T23:39:28.995Z","2026-01-26T23:39:29.079Z",{"id":1663,"documentId":1664,"title":1665,"createdAt":1666,"updatedAt":1666,"publishedAt":1667},377,"tyjt5k6gkfu4q5pu416mmmbj","Demographic groups","2026-01-26T23:39:29.000Z","2026-01-26T23:39:29.084Z",{"id":1669,"documentId":1670,"title":1671,"createdAt":1672,"updatedAt":1672,"publishedAt":1673},384,"kk8jftf46ld4loul1z8t6sk4","Biometric systems","2026-01-26T23:39:29.019Z","2026-01-26T23:39:29.131Z",{"id":1675,"documentId":1676,"title":1677,"createdAt":1678,"updatedAt":1678,"publishedAt":1679},380,"vyc5hr23wkxy7onar7gl4xwj","Recognition error rates","2026-01-26T23:39:29.041Z","2026-01-26T23:39:29.111Z",{"id":1681,"documentId":1682,"title":1683,"createdAt":1684,"updatedAt":1684,"publishedAt":1685},393,"w2glltwmpkkln92xr2toa1ss","Failure-to-enrol rate","2026-01-26T23:39:29.089Z","2026-01-26T23:39:29.212Z",{"id":1687,"documentId":1688,"title":1689,"createdAt":1690,"updatedAt":1690,"publishedAt":1691},389,"z298einjxkfyp245pz6o2h83","Statistical uncertainty","2026-01-26T23:39:29.141Z","2026-01-26T23:39:29.218Z",{"id":1693,"documentId":1694,"title":1695,"createdAt":1696,"updatedAt":1696,"publishedAt":1697},390,"p59h34c1291ynm3111o6f0j7","Equitability","2026-01-26T23:39:29.144Z","2026-01-26T23:39:29.222Z",{"id":33,"documentId":1699,"number":1700,"title":1701,"part":11,"abstract":1702,"lastUpdatedYear":524,"linkToOfficial":1703,"createdAt":1704,"updatedAt":1705,"publishedAt":1706,"featured":11,"image":11,"development_stage":1707,"domain":1710,"issuing_body":1716,"ai_standard_keywords":1717},"cwgvqsn1ikw9x1ivccrr9a09","ISO\u002FIEC 42006:2025","Information technology — Artificial intelligence — Requirements for bodies providing audit and certification of artificial intelligence management systems","\u003Cp>This scope sets out the additional requirements for bodies that audit and certify artificial intelligence management systems (AIMS) according to ISO\u002FIEC 42001. It builds on ISO\u002FIEC 17021-1 and ensures that certification bodies operate with the competence and rigour necessary to assess organisations developing, deploying or offering AI systems.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F42006","2026-01-27T14:06:02.299Z","2026-01-27T14:07:56.422Z","2026-01-27T14:07:56.476Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1708},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1709,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},2,"sy7vknfebz2lq0k9z3mbtaz6","Cross-industry","2026-01-26T14:25:29.845Z","2026-01-26T14:25:29.894Z",{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1718,1724,1730,1736,1742,1748,1749,1755],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},396,"datp4uukyvuawsv7un7edrsx","Artificial intelligence (AI)","2026-01-26T23:39:29.153Z","2026-01-26T23:39:29.286Z",{"id":1725,"documentId":1726,"title":1727,"createdAt":1728,"updatedAt":1728,"publishedAt":1729},419,"o0bna80sz4ojz98yg378fgfu","AI management system (AIMS)","2026-01-26T23:39:29.418Z","2026-01-26T23:39:29.517Z",{"id":1731,"documentId":1732,"title":1733,"createdAt":1734,"updatedAt":1734,"publishedAt":1735},417,"wdtzuij68co4jqz1hi4cqyck","Audit and certification","2026-01-26T23:39:29.425Z","2026-01-26T23:39:29.498Z",{"id":1737,"documentId":1738,"title":1739,"createdAt":1740,"updatedAt":1740,"publishedAt":1741},420,"h1eg8ub99e1d5e9lrcns9d1y","Conformity assessment bodies","2026-01-26T23:39:29.432Z","2026-01-26T23:39:29.563Z",{"id":1743,"documentId":1744,"title":1745,"createdAt":1746,"updatedAt":1746,"publishedAt":1747},418,"rq493y38rd3s7phppe4n2quv","ISO\u002FIEC 42001","2026-01-26T23:39:29.446Z","2026-01-26T23:39:29.509Z",{"id":641,"documentId":642,"title":643,"createdAt":644,"updatedAt":644,"publishedAt":645},{"id":1750,"documentId":1751,"title":1752,"createdAt":1753,"updatedAt":1753,"publishedAt":1754},421,"mitun30d6yyetx6idt8lzy0h","Competence","2026-01-26T23:39:29.523Z","2026-01-26T23:39:29.592Z",{"id":1756,"documentId":1757,"title":1758,"createdAt":1759,"updatedAt":1759,"publishedAt":1760},422,"cphcxswiarmmdj5aukkessr4","Reliability","2026-01-26T23:39:29.526Z","2026-01-26T23:39:29.594Z",{"id":352,"documentId":1762,"number":1763,"title":1764,"part":1765,"abstract":1766,"lastUpdatedYear":276,"linkToOfficial":1767,"createdAt":1768,"updatedAt":1769,"publishedAt":1770,"featured":11,"image":11,"development_stage":1771,"domain":1774,"issuing_body":1775,"ai_standard_keywords":1776},"qfvxe0ko1strgd20w5zj1pgg","ISO\u002FIEC 24029-2:2023","Artificial intelligence (AI) — Assessment of the robustness of neural networks","Part 2: Methodology for the use of formal methods","\u003Cp>This document provides methodology for the use of formal methods to assess robustness properties of neural networks. The document focuses on how to select, apply and manage formal methods to prove robustness properties.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F79804.html","2026-01-27T14:09:13.177Z","2026-01-27T14:10:58.473Z","2026-01-27T14:10:58.522Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1772},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1773,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1777,1778,1784,1790,1796,1802,1808],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},{"id":1779,"documentId":1780,"title":1781,"createdAt":1782,"updatedAt":1782,"publishedAt":1783},433,"n0u53v1dgvh5e9547a8sxci7","Robustness","2026-01-26T23:39:29.829Z","2026-01-26T23:39:29.905Z",{"id":1785,"documentId":1786,"title":1787,"createdAt":1788,"updatedAt":1788,"publishedAt":1789},441,"y07ldkfu03y4yz98f05hf0te","Neural networks","2026-01-26T23:39:29.833Z","2026-01-26T23:39:30.000Z",{"id":1791,"documentId":1792,"title":1793,"createdAt":1794,"updatedAt":1794,"publishedAt":1795},440,"cnuxozbfhyddswtj72ztduen","Formal methods","2026-01-26T23:39:29.836Z","2026-01-26T23:39:29.945Z",{"id":1797,"documentId":1798,"title":1799,"createdAt":1800,"updatedAt":1800,"publishedAt":1801},434,"gff0w4qp6xcs2sff4eoza9ro","Methodology","2026-01-26T23:39:29.840Z","2026-01-26T23:39:29.921Z",{"id":1803,"documentId":1804,"title":1805,"createdAt":1806,"updatedAt":1806,"publishedAt":1807},435,"ezrfz425am8u8n5cx1ge40wd","Robustness properties","2026-01-26T23:39:29.843Z","2026-01-26T23:39:29.925Z",{"id":1809,"documentId":1810,"title":1811,"createdAt":1812,"updatedAt":1812,"publishedAt":1813},436,"d734i9utcuyk9ojbu1amc2yg","Assessment","2026-01-26T23:39:29.847Z","2026-01-26T23:39:29.928Z",{"id":382,"documentId":1815,"number":1816,"title":1817,"part":11,"abstract":1818,"lastUpdatedYear":171,"linkToOfficial":1819,"createdAt":1820,"updatedAt":1821,"publishedAt":1822,"featured":11,"image":11,"development_stage":1823,"domain":1826,"issuing_body":1827,"ai_standard_keywords":1828},"jaysg7odjr6gvstbt23hots9","ISO\u002FIEC 23053:2022 ","Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)","\u003Cp>This document provides a conceptual framework and shared terminology for describing artificial intelligence (AI) systems that use machine learning (ML). It defines the components and functions of ML-based AI systems within the broader AI ecosystem, offering clarity on how these systems operate and interact. The standard is designed for both technical experts and non-specialists seeking to understand and explain ML systems in a structured, consistent way.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F74438.html","2026-01-27T14:24:29.613Z","2026-01-27T14:26:16.398Z","2026-01-27T14:26:16.464Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1824},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1825,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1829,1830,1836,1842,1848,1854],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},{"id":1831,"documentId":1832,"title":1833,"createdAt":1834,"updatedAt":1834,"publishedAt":1835},457,"detkz2ul6uxsa4v52pr5ithh","Machine learning (ML)","2026-01-26T23:39:30.089Z","2026-01-26T23:39:30.177Z",{"id":1837,"documentId":1838,"title":1839,"createdAt":1840,"updatedAt":1840,"publishedAt":1841},495,"hkqjamhsfap93i74kxeayozd","AI system framework","2026-01-26T23:39:30.504Z","2026-01-26T23:39:30.655Z",{"id":1843,"documentId":1844,"title":1845,"createdAt":1846,"updatedAt":1846,"publishedAt":1847},493,"dxq6zrb5pqgqaa0rfh3gljsv","Generic AI system","2026-01-26T23:39:30.560Z","2026-01-26T23:39:30.649Z",{"id":1849,"documentId":1850,"title":1851,"createdAt":1852,"updatedAt":1852,"publishedAt":1853},499,"lepnkc4nhwukz172g2c22gdj","System components","2026-01-26T23:39:30.573Z","2026-01-26T23:39:30.702Z",{"id":1855,"documentId":1856,"title":1857,"createdAt":1858,"updatedAt":1858,"publishedAt":1859},494,"njuz6rp0dsaoh93u4m0upgb2","AI ecosystem","2026-01-26T23:39:30.588Z","2026-01-26T23:39:30.664Z",{"id":1861,"documentId":1862,"number":1863,"title":1864,"part":1865,"abstract":1866,"lastUpdatedYear":1415,"linkToOfficial":1867,"createdAt":1868,"updatedAt":1869,"publishedAt":1870,"featured":11,"image":11,"development_stage":1871,"domain":1874,"issuing_body":1875,"ai_standard_keywords":1876},104,"dlvba5mr2jvf8grhigvs9con","ISO\u002FIEC 5259-1:2024","Artificial intelligence — Data quality for analytics and machine learning (ML)","Part 1: Overview, terminology, and examples","\u003Cp>This document is the foundational part of the ISO\u002FIEC 5259 series, focusing on data quality for analytics and machine learning (ML). This standard provides an overview, terminology, and illustrative examples to help organizations understand and apply the entire series effectively. It establishes the framework for assessing and enhancing data quality across different phases of the data life cycle, crucial for reliable analytics and ML outcomes.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81088.html","2026-01-27T14:26:56.276Z","2026-01-27T14:28:42.794Z","2026-01-27T14:28:42.848Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1872},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1873,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1877,1878,1884,1890,1891,1897,1898,1904,1910],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},{"id":1879,"documentId":1880,"title":1881,"createdAt":1882,"updatedAt":1882,"publishedAt":1883},498,"esp252r0wurmx1zqlj1m1o30","Data quality","2026-01-26T23:39:30.625Z","2026-01-26T23:39:30.719Z",{"id":1885,"documentId":1886,"title":1887,"createdAt":1888,"updatedAt":1888,"publishedAt":1889},500,"fd1bg624h8aov50ew7lkowam","Analytics","2026-01-26T23:39:30.639Z","2026-01-26T23:39:30.729Z",{"id":1831,"documentId":1832,"title":1833,"createdAt":1834,"updatedAt":1834,"publishedAt":1835},{"id":1892,"documentId":1893,"title":1894,"createdAt":1895,"updatedAt":1895,"publishedAt":1896},503,"rktuo0jomizky6qmmf2bb6nw","Overview","2026-01-26T23:39:30.662Z","2026-01-26T23:39:30.757Z",{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},{"id":1899,"documentId":1900,"title":1901,"createdAt":1902,"updatedAt":1902,"publishedAt":1903},504,"d2u0wc2a83r8iapeiau16bu1","Conceptual understanding","2026-01-26T23:39:30.678Z","2026-01-26T23:39:30.770Z",{"id":1905,"documentId":1906,"title":1907,"createdAt":1908,"updatedAt":1908,"publishedAt":1909},513,"in92jzqoghnkufnv1hgfb2gr","Examples","2026-01-26T23:39:30.711Z","2026-01-26T23:39:30.861Z",{"id":1911,"documentId":1912,"title":1913,"createdAt":1914,"updatedAt":1914,"publishedAt":1915},510,"jelkrru3e2zod1ap8i03tl7p","Use cases","2026-01-26T23:39:30.708Z","2026-01-26T23:39:30.832Z",{"id":1917,"documentId":1918,"number":1919,"title":1864,"part":1920,"abstract":1921,"lastUpdatedYear":1415,"linkToOfficial":1922,"createdAt":1923,"updatedAt":1924,"publishedAt":1925,"featured":11,"image":11,"development_stage":1926,"domain":1929,"issuing_body":1930,"ai_standard_keywords":1931},106,"hikmno7oep6qr23zpr3pr75a","ISO\u002FIEC 5259-2:2024","Part 2: Data quality measures","\u003Cp>This document defines a data quality model and a set of measurable characteristics to help organisations assess and report on data quality in the context of analytics and machine learning (ML). It builds on existing standards (such as ISO\u002FIEC 25012 and ISO 8000) and provides a common foundation for ensuring that data used in AI and analytics processes is trustworthy and fit for purpose.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81860.html","2026-01-27T14:28:54.001Z","2026-01-27T14:30:26.303Z","2026-01-27T14:30:26.387Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1927},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1928,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1932,1933,1934,1935,1936,1942,1948,1954],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},{"id":1879,"documentId":1880,"title":1881,"createdAt":1882,"updatedAt":1882,"publishedAt":1883},{"id":1885,"documentId":1886,"title":1887,"createdAt":1888,"updatedAt":1888,"publishedAt":1889},{"id":1831,"documentId":1832,"title":1833,"createdAt":1834,"updatedAt":1834,"publishedAt":1835},{"id":1937,"documentId":1938,"title":1939,"createdAt":1940,"updatedAt":1940,"publishedAt":1941},512,"uujqqj0zvhzimhxqy1hp9jtj","Data quality model","2026-01-26T23:39:30.765Z","2026-01-26T23:39:30.858Z",{"id":1943,"documentId":1944,"title":1945,"createdAt":1946,"updatedAt":1946,"publishedAt":1947},514,"d2cu15uuus7x1gxm4gofw1r2","Data quality measures","2026-01-26T23:39:30.784Z","2026-01-26T23:39:30.880Z",{"id":1949,"documentId":1950,"title":1951,"createdAt":1952,"updatedAt":1952,"publishedAt":1953},518,"kzpxtpnik2p22uhupjjksxw3","Reporting","2026-01-26T23:39:30.812Z","2026-01-26T23:39:30.966Z",{"id":1955,"documentId":1956,"title":1957,"createdAt":1958,"updatedAt":1958,"publishedAt":1959},515,"u4gaei0thkzl3a80lkgxqprf","Objectives","2026-01-26T23:39:30.826Z","2026-01-26T23:39:30.933Z",{"id":425,"documentId":1961,"number":1962,"title":1864,"part":1963,"abstract":1964,"lastUpdatedYear":1415,"linkToOfficial":1965,"createdAt":1966,"updatedAt":1967,"publishedAt":1968,"featured":11,"image":11,"development_stage":1969,"domain":1972,"issuing_body":1973,"ai_standard_keywords":1974},"ntmq51fzyeikky7idatp4swu","ISO\u002FIEC 5259-3:2024","Part 3: Data quality management requirements and guidelines","\u003Cp>This document defines the requirements and guidance for managing the quality of data used in analytics and machine learning (ML). It provides a flexible framework for setting up a data quality management system (DQMS) that can adapt to various AI life cycles, ensuring that the data powering ML systems is reliable, auditable and aligned with stakeholder expectations.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81092.html","2026-01-27T14:30:35.945Z","2026-01-27T14:31:56.187Z","2026-01-27T14:31:56.236Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":1970},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":1971,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[1975,1976,1977,1978,1979,1985,1986,1987,1993],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},{"id":1879,"documentId":1880,"title":1881,"createdAt":1882,"updatedAt":1882,"publishedAt":1883},{"id":1885,"documentId":1886,"title":1887,"createdAt":1888,"updatedAt":1888,"publishedAt":1889},{"id":1831,"documentId":1832,"title":1833,"createdAt":1834,"updatedAt":1834,"publishedAt":1835},{"id":1980,"documentId":1981,"title":1982,"createdAt":1983,"updatedAt":1983,"publishedAt":1984},519,"s0viqeo8xanaimfos3do4y5c","Data quality management","2026-01-26T23:39:30.837Z","2026-01-26T23:39:30.947Z",{"id":641,"documentId":642,"title":643,"createdAt":644,"updatedAt":644,"publishedAt":645},{"id":265,"documentId":266,"title":267,"createdAt":268,"updatedAt":268,"publishedAt":269},{"id":1988,"documentId":1989,"title":1990,"createdAt":1991,"updatedAt":1991,"publishedAt":1992},521,"dq46rpqzqtc27igmc9ogplbz","Quality management process","2026-01-26T23:39:30.873Z","2026-01-26T23:39:30.998Z",{"id":1994,"documentId":1995,"title":1996,"createdAt":1997,"updatedAt":1997,"publishedAt":1998},517,"iexet3x0wrlkcfympcunqxwx","Reference process","2026-01-26T23:39:30.879Z","2026-01-26T23:39:30.954Z",{"id":457,"documentId":2000,"number":2001,"title":1864,"part":2002,"abstract":2003,"lastUpdatedYear":1415,"linkToOfficial":2004,"createdAt":2005,"updatedAt":2006,"publishedAt":2007,"featured":11,"image":11,"development_stage":2008,"domain":2011,"issuing_body":2012,"ai_standard_keywords":2013},"jz9uvhjqp8trjlutlbmqhgkx","ISO\u002FIEC 5259-4:2024","Part 4: Data quality process framework","\u003Cp>This document establishes general common organizational approaches, regardless of the type, size or nature of the applying organization, to ensure data quality for training and evaluation in analytics and machine learning (ML). It includes guidance on the data quality process for supervised ML with regard to the labelling of data used for training ML systems, including common organizational approaches for training data labelling, unsupervised ML, semi-supervised ML, reinforcement learning and analytics. This document is applicable to training and evaluation data that come from different sources, including data acquisition and data composition, data preparation, data labelling, evaluation and data use. This document does not define specific services, platforms or tools.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F81093.html","2026-01-27T14:32:05.673Z","2026-01-27T14:33:42.959Z","2026-01-27T14:33:43.001Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":2009},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2010,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2014,2015,2016,2017,2018,2024,2030,2036,2042,2048],{"id":1719,"documentId":1720,"title":1721,"createdAt":1722,"updatedAt":1722,"publishedAt":1723},{"id":1879,"documentId":1880,"title":1881,"createdAt":1882,"updatedAt":1882,"publishedAt":1883},{"id":1885,"documentId":1886,"title":1887,"createdAt":1888,"updatedAt":1888,"publishedAt":1889},{"id":1831,"documentId":1832,"title":1833,"createdAt":1834,"updatedAt":1834,"publishedAt":1835},{"id":2019,"documentId":2020,"title":2021,"createdAt":2022,"updatedAt":2022,"publishedAt":2023},520,"x5xsyaoof1an5xhnjj1doc1f","Data quality process framework","2026-01-26T23:39:30.901Z","2026-01-26T23:39:30.971Z",{"id":2025,"documentId":2026,"title":2027,"createdAt":2028,"updatedAt":2028,"publishedAt":2029},522,"fawcgfhz1ssick35vt83g11s","Training data","2026-01-26T23:39:30.964Z","2026-01-26T23:39:31.062Z",{"id":2031,"documentId":2032,"title":2033,"createdAt":2034,"updatedAt":2034,"publishedAt":2035},533,"vjzyjout1pab4te6k7qcx4tu","Evaluation data","2026-01-26T23:39:31.289Z","2026-01-26T23:39:31.392Z",{"id":2037,"documentId":2038,"title":2039,"createdAt":2040,"updatedAt":2040,"publishedAt":2041},534,"kxz5s1tm13279uuk1w2hgy2x","Data labelling","2026-01-26T23:39:31.293Z","2026-01-26T23:39:31.396Z",{"id":2043,"documentId":2044,"title":2045,"createdAt":2046,"updatedAt":2046,"publishedAt":2047},535,"w0x3yphfjlubyrdc2mipsqj4","Data acquisition","2026-01-26T23:39:31.297Z","2026-01-26T23:39:31.401Z",{"id":2049,"documentId":2050,"title":2051,"createdAt":2052,"updatedAt":2052,"publishedAt":2053},536,"y17qhwupjy1y397wqqn2h7pl","Data preparation","2026-01-26T23:39:31.301Z","2026-01-26T23:39:31.405Z",{"id":2055,"documentId":2056,"number":2057,"title":2058,"part":11,"abstract":2059,"lastUpdatedYear":276,"linkToOfficial":2060,"createdAt":2061,"updatedAt":2062,"publishedAt":2063,"featured":11,"image":11,"development_stage":2064,"domain":2067,"issuing_body":2068,"ai_standard_keywords":2069},116,"xv02hg8sedvloabecf70zebr","ISO\u002FIEC 25059:2023","Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems","\u003Cp>This document outlines a quality model for AI systems and is an application-specific extension to the standards on SQuaRE. The characteristics and sub-characteristics detailed in the model provide consistent terminology for specifying, measuring and evaluating AI system quality. The characteristics and sub-characteristics detailed in the model also provide a set of quality characteristics against which stated quality requirements can be compared for completeness\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F80655.html","2026-01-27T14:38:52.952Z","2026-01-27T14:40:27.400Z","2026-01-27T14:40:27.450Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":2065},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2066,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2070,2076,2082,2088,2094,2100,2106,2107],{"id":2071,"documentId":2072,"title":2073,"createdAt":2074,"updatedAt":2074,"publishedAt":2075},558,"tdkotj8hxmw1i2dmb5ax8dln","Software engineering","2026-01-26T23:39:31.521Z","2026-01-26T23:39:31.614Z",{"id":2077,"documentId":2078,"title":2079,"createdAt":2080,"updatedAt":2080,"publishedAt":2081},560,"ci1p7cqcp961abhy0tl3c7o4","SQuaRE","2026-01-26T23:39:31.536Z","2026-01-26T23:39:31.642Z",{"id":2083,"documentId":2084,"title":2085,"createdAt":2086,"updatedAt":2086,"publishedAt":2087},562,"oc4nszgwg11fwpvwh9d029ni","Quality model for AI systems","2026-01-26T23:39:31.582Z","2026-01-26T23:39:31.670Z",{"id":2089,"documentId":2090,"title":2091,"createdAt":2092,"updatedAt":2092,"publishedAt":2093},568,"rae0mkzykzcuostsvz7969x5","Application-specific extension","2026-01-26T23:39:31.639Z","2026-01-26T23:39:31.743Z",{"id":2095,"documentId":2096,"title":2097,"createdAt":2098,"updatedAt":2098,"publishedAt":2099},571,"glos694yxmo8646fsf39g2ww","Characteristics","2026-01-26T23:39:31.685Z","2026-01-26T23:39:31.769Z",{"id":2101,"documentId":2102,"title":2103,"createdAt":2104,"updatedAt":2104,"publishedAt":2105},572,"vvd8sp226gwf80524ez10bq4","Sub-characteristics","2026-01-26T23:39:31.697Z","2026-01-26T23:39:31.784Z",{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},{"id":2108,"documentId":2109,"title":2110,"createdAt":2111,"updatedAt":2111,"publishedAt":2112},573,"ubz9peejiuuo1yq15e7lghr0","Evaluation","2026-01-26T23:39:31.720Z","2026-01-26T23:39:31.798Z",{"id":514,"documentId":2114,"number":2115,"title":2058,"part":11,"abstract":2116,"lastUpdatedYear":276,"linkToOfficial":2117,"createdAt":2118,"updatedAt":2119,"publishedAt":2120,"featured":11,"image":11,"development_stage":2121,"domain":2139,"issuing_body":2140,"ai_standard_keywords":2141},"yzfjmqfvg3dn3wh6hf14yfek","ISO\u002FIEC DIS 25059","\u003Cp>This document is an extension of the SQuaRE series that defines a quality model specifically for artificial intelligence (AI) systems. It adapts the principles of software and systems quality — as described in ISO\u002FIEC 25010 — to address the unique properties of AI, such as probabilistic outcomes, learning behaviours, and reliance on data. This model helps organisations define, evaluate and measure AI quality consistently across products and tasks\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F88234.html","2026-01-27T14:40:43.928Z","2026-01-27T14:41:42.305Z","2026-01-27T14:41:42.404Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2128},10,"ai93273mfbbpv3ecmasrpgnw","Draft","2026-01-26T14:24:47.542Z","2026-01-28T19:50:43.367Z","2026-01-28T19:50:43.448Z",{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2132,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},162,"u62ust4eq6p4ptwv02o01cq0","i-draft.svg",{},"i_draft_aaf62150e3",0.51,"\u002Fuploads\u002Fi_draft_aaf62150e3.svg","2026-01-28T14:44:56.764Z","2026-01-28T19:50:41.363Z","2026-01-28T14:44:56.765Z",{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2142,2143,2144,2145,2146,2147,2148,2149],{"id":2071,"documentId":2072,"title":2073,"createdAt":2074,"updatedAt":2074,"publishedAt":2075},{"id":2077,"documentId":2078,"title":2079,"createdAt":2080,"updatedAt":2080,"publishedAt":2081},{"id":2083,"documentId":2084,"title":2085,"createdAt":2086,"updatedAt":2086,"publishedAt":2087},{"id":2089,"documentId":2090,"title":2091,"createdAt":2092,"updatedAt":2092,"publishedAt":2093},{"id":2095,"documentId":2096,"title":2097,"createdAt":2098,"updatedAt":2098,"publishedAt":2099},{"id":2101,"documentId":2102,"title":2103,"createdAt":2104,"updatedAt":2104,"publishedAt":2105},{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},{"id":2108,"documentId":2109,"title":2110,"createdAt":2111,"updatedAt":2111,"publishedAt":2112},{"id":552,"documentId":2151,"number":2152,"title":2153,"part":11,"abstract":2154,"lastUpdatedYear":276,"linkToOfficial":2155,"createdAt":2156,"updatedAt":2157,"publishedAt":2158,"featured":11,"image":11,"development_stage":2159,"domain":2162,"issuing_body":2163,"ai_standard_keywords":2164},"bj5gimdjcsyny5u2lrdousch","ISO\u002FIEC 24392:2023","Cybersecurity — Security reference model for industrial internet platform (SRM- IIP)","\u003Cp>This document presents specific characteristics of industrial internet platforms (IIPs), including related security threats, context-specific security control objectives and security controls. This document covers specific security concerns in the industrial context and thus complements generic security standards and reference models. In particular, this document includes secure data collection and transmission among industrial devices, data security of industrial cloud platforms, and secure collaborations with various industry stakeholders. The users of this document are organizations who develop, operate, or use any components of IIPs, including third parties who provide services to the abovementioned stakeholders. 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This document applies to all types of organizations involved in any of the lifecycle stages of AI systems as well as to any AI stakeholder 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This document provides AI providers and AI producers with a set of characteristics and the related optimizations that they can use to enhance their machine learning model training efficiency. AI providers and AI producers can also use this information to evaluate different machine learning model training approaches. This document does not specify any training accelerating mechanisms provided and implemented within machine learning computing device defined in ISO \u002FIEC TR 17903 and its library.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F88246.html","2026-01-27T14:51:21.017Z","2026-01-27T14:52:55.638Z","2026-01-27T14:52:55.672Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2390},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2391,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2395,2396,2397,2403,2404,2410,2411,2412,2413],{"id":2166,"documentId":2167,"title":2168,"createdAt":2169,"updatedAt":2169,"publishedAt":2170},{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2398,"documentId":2399,"title":2400,"createdAt":2401,"updatedAt":2401,"publishedAt":2402},614,"kgejdttoupigw0m8o3a6ujg0","Lightweight AI systems","2026-01-26T23:39:32.115Z","2026-01-26T23:39:32.142Z",{"id":2325,"documentId":2326,"title":2327,"createdAt":2328,"updatedAt":2328,"publishedAt":2329},{"id":2405,"documentId":2406,"title":2407,"createdAt":2408,"updatedAt":2408,"publishedAt":2409},615,"v682ptfv3cmc6xqhf92m0awp","Limited resource utilization","2026-01-26T23:39:32.155Z","2026-01-26T23:39:32.226Z",{"id":559,"documentId":560,"title":561,"createdAt":562,"updatedAt":562,"publishedAt":563},{"id":254,"documentId":255,"title":256,"createdAt":257,"updatedAt":257,"publishedAt":258},{"id":2362,"documentId":2363,"title":2364,"createdAt":2365,"updatedAt":2365,"publishedAt":2366},{"id":2414,"documentId":2415,"title":2416,"createdAt":2417,"updatedAt":2417,"publishedAt":2418},616,"g0g7auyju5i2pve2wut3e3hm","Optimization","2026-01-26T23:39:32.160Z","2026-01-26T23:39:32.231Z",{"id":583,"documentId":2420,"number":2421,"title":2422,"part":11,"abstract":2423,"lastUpdatedYear":1415,"linkToOfficial":2424,"createdAt":2425,"updatedAt":2426,"publishedAt":2427,"featured":11,"image":11,"development_stage":2428,"domain":2431,"issuing_body":2432,"ai_standard_keywords":2433},"tci2zwt4pazohrvq1l2xukk2","ISO\u002FIEC AWI TS 42111","Information technology — Artificial intelligence — Guidance on lightweight AI systems","\u003Cp>This document provides guidance on developing and deploying lightweight AI systems optimized for limited resource utilization. The optimization approach for AI model training efficiency is not within the scope of the this document.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F88245.html","2026-01-27T14:53:00.903Z","2026-01-27T14:54:39.265Z","2026-01-27T14:54:39.308Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2429},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2430,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2434,2435,2436,2442,2443,2444,2450,2456],{"id":2166,"documentId":2167,"title":2168,"createdAt":2169,"updatedAt":2169,"publishedAt":2170},{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2437,"documentId":2438,"title":2439,"createdAt":2440,"updatedAt":2440,"publishedAt":2441},617,"naj2bu8bkfcjp1urxajfhnfl","Societal concerns","2026-01-26T23:39:32.163Z","2026-01-26T23:39:32.234Z",{"id":293,"documentId":294,"title":295,"createdAt":296,"updatedAt":296,"publishedAt":297},{"id":2325,"documentId":2326,"title":2327,"createdAt":2328,"updatedAt":2328,"publishedAt":2329},{"id":2445,"documentId":2446,"title":2447,"createdAt":2448,"updatedAt":2448,"publishedAt":2449},618,"qgf0gjb5lqy4gcp5nncv6wj1","AI systems life cycle","2026-01-26T23:39:32.166Z","2026-01-26T23:39:32.237Z",{"id":2451,"documentId":2452,"title":2453,"createdAt":2454,"updatedAt":2454,"publishedAt":2455},620,"an51db0yvr0vreadln045tkd","Governance","2026-01-26T23:39:32.169Z","2026-01-26T23:39:32.255Z",{"id":2457,"documentId":2458,"title":2459,"createdAt":2460,"updatedAt":2460,"publishedAt":2461},622,"ed5g30hvxdop42xkl48uguqw","Impact assessment","2026-01-26T23:39:32.172Z","2026-01-26T23:39:32.257Z",{"id":611,"documentId":2463,"number":2464,"title":2465,"part":11,"abstract":2466,"lastUpdatedYear":524,"linkToOfficial":2467,"createdAt":2468,"updatedAt":2469,"publishedAt":2470,"featured":11,"image":11,"development_stage":2471,"domain":2474,"issuing_body":2475,"ai_standard_keywords":2476},"t6jg6zzrs3d8exvebfs55but","ISO\u002FIEC CD TS 22443","Information technology — Artificial intelligence — Guidance on addressing societal concerns and ethical considerations","\u003Cp>This document provides guidance on how an organization can identify and address societal concerns and ethical considerations during the life cycle of AI systems that can potentially harm individuals and society. The document expands existing AI system governance, management system and impact assessment standards. This document is applicable to all types of organizations that develop or use AI systems, regardless of size, type and nature.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F87119.html","2026-01-27T14:54:48.298Z","2026-01-27T14:56:43.693Z","2026-01-27T14:56:43.726Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2472},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2473,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2477,2478,2479,2485,2486,2492,2498],{"id":2166,"documentId":2167,"title":2168,"createdAt":2169,"updatedAt":2169,"publishedAt":2170},{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2480,"documentId":2481,"title":2482,"createdAt":2483,"updatedAt":2483,"publishedAt":2484},621,"mx5wottymtfnz75dqqxrn808","Human oversight","2026-01-26T23:39:32.189Z","2026-01-26T23:39:32.268Z",{"id":2325,"documentId":2326,"title":2327,"createdAt":2328,"updatedAt":2328,"publishedAt":2329},{"id":2487,"documentId":2488,"title":2489,"createdAt":2490,"updatedAt":2490,"publishedAt":2491},619,"cu8abb6exvljrkgzqzx4jdoi","Human control","2026-01-26T23:39:32.181Z","2026-01-26T23:39:32.246Z",{"id":2493,"documentId":2494,"title":2495,"createdAt":2496,"updatedAt":2496,"publishedAt":2497},633,"a297mh4nf806pi2j7jzrpbhp","Monitoring","2026-01-26T23:39:32.618Z","2026-01-26T23:39:32.710Z",{"id":2499,"documentId":2500,"title":2501,"createdAt":2502,"updatedAt":2502,"publishedAt":2503},566,"nupvaoxdzhse4vxbuvwf6qm0","AI system life cycle","2026-01-26T23:39:31.525Z","2026-01-26T23:39:31.666Z",{"id":623,"documentId":2505,"number":2506,"title":2507,"part":11,"abstract":2508,"lastUpdatedYear":524,"linkToOfficial":2509,"createdAt":2510,"updatedAt":2511,"publishedAt":2512,"featured":11,"image":11,"development_stage":2513,"domain":2516,"issuing_body":2517,"ai_standard_keywords":2518},"gmsxtiapuoipj1v98akgrlzm","ISO\u002FIEC DIS 42105","Information technology — Artificial intelligence — Guidance for human oversight of AI systems","\u003Cp>This document provides guidance on human control and monitoring of AI systems, which is referred to as human oversight. This document extends ISO\u002FIEC TS 8200. This document is applicable to all types of organizations. This document is applicable throughout the AI system life cycle.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F86902.html","2026-01-27T14:56:49.533Z","2026-01-27T14:58:18.385Z","2026-01-27T14:58:18.429Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2514},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2515,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2519,2520,2521,2527,2528,2529,2530,2531],{"id":2166,"documentId":2167,"title":2168,"createdAt":2169,"updatedAt":2169,"publishedAt":2170},{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2522,"documentId":2523,"title":2524,"createdAt":2525,"updatedAt":2525,"publishedAt":2526},640,"k0to9e0ynet1fwsgsmi7dgw4","Uncertainty quantification","2026-01-26T23:39:32.622Z","2026-01-26T23:39:32.745Z",{"id":2271,"documentId":2272,"title":2273,"createdAt":2274,"updatedAt":2274,"publishedAt":2275},{"id":2325,"documentId":2326,"title":2327,"createdAt":2328,"updatedAt":2328,"publishedAt":2329},{"id":641,"documentId":642,"title":643,"createdAt":644,"updatedAt":644,"publishedAt":645},{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},{"id":2499,"documentId":2500,"title":2501,"createdAt":2502,"updatedAt":2502,"publishedAt":2503},{"id":635,"documentId":2533,"number":2534,"title":2535,"part":11,"abstract":2536,"lastUpdatedYear":1415,"linkToOfficial":2537,"createdAt":2538,"updatedAt":2539,"publishedAt":2540,"featured":11,"image":11,"development_stage":2541,"domain":2544,"issuing_body":2545,"ai_standard_keywords":2546},"zw2e5l89m6630o28s9bccivv","ISO\u002FIEC AWI TS 25223","Information technology — Artificial intelligence — Guidance and requirements for uncertainty quantification in AI systems","\u003Cp>This document specifies general and technical guidance and requirements for the development and use of methods for the quantification of uncertainties in AI systems. This document defines fundamental terminology for uncertainty quantification in AI systems along with the characteristics of selected approaches to uncertainty quantification. The characteristics and approaches are then described through selected applications. This document describes aspects of quantification of uncertainties for all stages of the AI system life cycle.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F89475.html","2026-01-27T14:58:24.326Z","2026-01-27T15:00:03.297Z","2026-01-27T15:00:03.465Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2542},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2543,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2547,2548,2549,2550,2556,2557,2558],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":1779,"documentId":1780,"title":1781,"createdAt":1782,"updatedAt":1782,"publishedAt":1783},{"id":1785,"documentId":1786,"title":1787,"createdAt":1788,"updatedAt":1788,"publishedAt":1789},{"id":2551,"documentId":2552,"title":2553,"createdAt":2554,"updatedAt":2554,"publishedAt":2555},649,"dh2lizjy6ip3me3emonxkw73","Statistical methods","2026-01-26T23:39:32.655Z","2026-01-26T23:39:32.785Z",{"id":1797,"documentId":1798,"title":1799,"createdAt":1800,"updatedAt":1800,"publishedAt":1801},{"id":1803,"documentId":1804,"title":1805,"createdAt":1806,"updatedAt":1806,"publishedAt":1807},{"id":1809,"documentId":1810,"title":1811,"createdAt":1812,"updatedAt":1812,"publishedAt":1813},{"id":2560,"documentId":2561,"number":2562,"title":1764,"part":2563,"abstract":2564,"lastUpdatedYear":524,"linkToOfficial":2565,"createdAt":2566,"updatedAt":2567,"publishedAt":2568,"featured":11,"image":11,"development_stage":2569,"domain":2572,"issuing_body":2573,"ai_standard_keywords":2574},142,"n6r23qzyijdc3xxy7u3uzx79","ISO\u002FIEC CD 24029-3","Part 3: Methodology for the use of statistical methods","\u003Cp>This document provides methodology for the use of statistical methods to assess robustness properties of neural networks. The document focuses on how to select, apply and manage statistical methods to assess robustness properties.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F86901.html","2026-01-27T15:00:09.822Z","2026-01-27T15:02:15.823Z","2026-01-27T15:02:15.873Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2570},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2571,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2575,2576,2582,2588,2589,2595,2601,2607],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2577,"documentId":2578,"title":2579,"createdAt":2580,"updatedAt":2580,"publishedAt":2581},634,"s1b4sv80pbdu1mfsfzanvvh3","Testing","2026-01-26T23:39:32.626Z","2026-01-26T23:39:32.716Z",{"id":2583,"documentId":2584,"title":2585,"createdAt":2586,"updatedAt":2586,"publishedAt":2587},635,"jygzy9e9769dw34vddste9vk","Red teaming","2026-01-26T23:39:32.630Z","2026-01-26T23:39:32.722Z",{"id":2325,"documentId":2326,"title":2327,"createdAt":2328,"updatedAt":2328,"publishedAt":2329},{"id":2590,"documentId":2591,"title":2592,"createdAt":2593,"updatedAt":2593,"publishedAt":2594},636,"wvdnd1qa95vixu3meeppqqtd","Security risks","2026-01-26T23:39:32.633Z","2026-01-26T23:39:32.725Z",{"id":2596,"documentId":2597,"title":2598,"createdAt":2599,"updatedAt":2599,"publishedAt":2600},637,"jps1c8delo0ck7xkaeznjmea","Attack vectors","2026-01-26T23:39:32.636Z","2026-01-26T23:39:32.729Z",{"id":2602,"documentId":2603,"title":2604,"createdAt":2605,"updatedAt":2605,"publishedAt":2606},638,"vizcwzyyfa77atxcglkuzcix","Methodologies","2026-01-26T23:39:32.639Z","2026-01-26T23:39:32.732Z",{"id":2608,"documentId":2609,"title":2610,"createdAt":2611,"updatedAt":2611,"publishedAt":2612},639,"d0hni3p7s6ignhun31r88kkk","AI system lifecycle","2026-01-26T23:39:32.643Z","2026-01-26T23:39:32.738Z",{"id":2614,"documentId":2615,"number":2616,"title":2617,"part":2618,"abstract":2619,"lastUpdatedYear":524,"linkToOfficial":2620,"createdAt":2621,"updatedAt":2622,"publishedAt":2623,"featured":11,"image":11,"development_stage":2624,"domain":2627,"issuing_body":2628,"ai_standard_keywords":2629},144,"j4mlfamys2ba1yn89u9nesv8","ISO\u002FIEC AWI TS 42119-7","Artificial intelligence — Testing of AI","Part 7: Red teaming","\u003Cp>This document provides technology-agnostic guidance for conducting red teaming assessments on AI systems. The document covers: — Definitions and terminology specific to red teaming in AI contexts — Identification of risks, applicability, objectives and attack vectors — Methodologies for planning and executing red teaming assessments on AI systems, aligned with ISO\u002FIEC 29119 processes as appropriate — Procedures for documenting and reporting red teaming findings — Recommendations for integrating red teaming into the AI system lifecycle This document is applicable to testing all types of AI systems.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F91240.html","2026-01-27T15:02:23.049Z","2026-01-27T15:04:21.850Z","2026-01-27T15:04:21.913Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2625},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2626,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2630,2631,2632,2638,2644,2650,2651,2652],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2577,"documentId":2578,"title":2579,"createdAt":2580,"updatedAt":2580,"publishedAt":2581},{"id":2633,"documentId":2634,"title":2635,"createdAt":2636,"updatedAt":2636,"publishedAt":2637},650,"lbx4iacp1bjy8ii5kuo9p740","Generative AI","2026-01-26T23:39:32.678Z","2026-01-26T23:39:32.809Z",{"id":2639,"documentId":2640,"title":2641,"createdAt":2642,"updatedAt":2642,"publishedAt":2643},659,"x6jvdg4c9os8l9bpahzxeb0z","Prompt-based text-to-text systems","2026-01-26T23:39:32.807Z","2026-01-26T23:39:32.889Z",{"id":2645,"documentId":2646,"title":2647,"createdAt":2648,"updatedAt":2648,"publishedAt":2649},651,"c61viyym4vby9rdfk1mrezq6","Quality assessment","2026-01-26T23:39:32.814Z","2026-01-26T23:39:32.870Z",{"id":112,"documentId":113,"title":114,"createdAt":115,"updatedAt":115,"publishedAt":116},{"id":2583,"documentId":2584,"title":2585,"createdAt":2586,"updatedAt":2586,"publishedAt":2587},{"id":2108,"documentId":2109,"title":2110,"createdAt":2111,"updatedAt":2111,"publishedAt":2112},{"id":2654,"documentId":2655,"number":2656,"title":2617,"part":2657,"abstract":2658,"lastUpdatedYear":524,"linkToOfficial":2659,"createdAt":2660,"updatedAt":2661,"publishedAt":2662,"featured":11,"image":11,"development_stage":2663,"domain":2666,"issuing_body":2667,"ai_standard_keywords":2668},146,"vvpyu4e8ran6x3d8oway4o1i","ISO\u002FIEC AWI TS 42119-8","Part 8: Quality assessment of prompt-based text-to-text systems that utilize generative AI","\u003Cp>This document provides definitions, concepts, requirements and guidance related to assessing prompt-based text-to-text AI systems that utilize generative AI. It covers quality assessment (which encompasses safety) using methodologies including red teaming and builds on existing standards such as ISO\u002FIEC 23282 and ISO\u002FIEC 42119-7. Additionally, it details methods for analyzing and interpreting results, as well as best practices for documentation.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F91609.html","2026-01-27T15:04:29.184Z","2026-01-27T15:05:35.709Z","2026-01-27T15:05:35.768Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2664},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2665,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2669,2670,2676,2677],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2671,"documentId":2672,"title":2673,"createdAt":2674,"updatedAt":2674,"publishedAt":2675},652,"cj6g2zttvl9pueh1shk4lmt7","Functional safety","2026-01-26T23:39:32.817Z","2026-01-26T23:39:32.873Z",{"id":2271,"documentId":2272,"title":2273,"createdAt":2274,"updatedAt":2274,"publishedAt":2275},{"id":641,"documentId":642,"title":643,"createdAt":644,"updatedAt":644,"publishedAt":645},{"id":2679,"documentId":2680,"number":2681,"title":2682,"part":2683,"abstract":2183,"lastUpdatedYear":1415,"linkToOfficial":2684,"createdAt":2685,"updatedAt":2686,"publishedAt":2687,"featured":11,"image":11,"development_stage":2688,"domain":2691,"issuing_body":2692,"ai_standard_keywords":2693},148,"xpl5qirp1udfug6rhba4s94e","ISO\u002FIEC AWI TS 22440-1","Artificial intelligence — Functional safety and AI systems","Part 1: Requirements","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F89535.html","2026-01-27T15:05:56.104Z","2026-01-27T15:07:26.545Z","2026-01-27T15:07:26.625Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2689},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2690,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2694,2695,2696,2697],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2671,"documentId":2672,"title":2673,"createdAt":2674,"updatedAt":2674,"publishedAt":2675},{"id":2271,"documentId":2272,"title":2273,"createdAt":2274,"updatedAt":2274,"publishedAt":2275},{"id":2325,"documentId":2326,"title":2327,"createdAt":2328,"updatedAt":2328,"publishedAt":2329},{"id":2699,"documentId":2700,"number":2701,"title":2682,"part":2702,"abstract":2183,"lastUpdatedYear":1415,"linkToOfficial":2703,"createdAt":2704,"updatedAt":2705,"publishedAt":2706,"featured":11,"image":11,"development_stage":2707,"domain":2710,"issuing_body":2711,"ai_standard_keywords":2712},150,"xpnepcbko4dxtb7w8ctfi6f6","ISO\u002FIEC AWI TS 22440-2","Part 2: 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It provides a definition of evaluation methods for those systems, together with guidance on how to select, implement and interpret those evaluation methods. This document covers quantitative metrics as well as other evaluation methods. It includes requirements on the implementation of the described metrics, and further requirements on the technical resources involved in the evaluation process.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F87387.html","2026-01-27T15:10:36.328Z","2026-01-27T15:11:52.262Z","2026-01-27T15:11:52.370Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2784},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2785,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2789,2790,2791,2792,2797,2803],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":142,"documentId":143,"title":144,"createdAt":145,"updatedAt":145,"publishedAt":146},{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},{"id":2793,"documentId":2794,"title":89,"createdAt":2795,"updatedAt":2795,"publishedAt":2796},672,"ephanx52v21zbn55ks41ch9x","2026-01-26T23:39:33.006Z","2026-01-26T23:39:33.020Z",{"id":2798,"documentId":2799,"title":2800,"createdAt":2801,"updatedAt":2801,"publishedAt":2802},485,"smflyf5jz2ylv8oxy0g0671m","Standardization","2026-01-26T23:39:30.449Z","2026-01-26T23:39:30.548Z",{"id":699,"documentId":700,"title":701,"createdAt":702,"updatedAt":702,"publishedAt":703},{"id":677,"documentId":2805,"number":2806,"title":2807,"part":2808,"abstract":2809,"lastUpdatedYear":1415,"linkToOfficial":2810,"createdAt":2811,"updatedAt":2812,"publishedAt":2813,"featured":11,"image":11,"development_stage":2814,"domain":2817,"issuing_body":2818,"ai_standard_keywords":2819},"nw5npric52f7rxvffqbripo3","ISO\u002FIEC AWI 22989-2","Artificial intelligence — Concepts and terminology","Part 2: Healthcare","\u003Cp>This document establishes terminology for Al and describes concepts in the field of AI for healthcare. This document can be used in the development of other standards and in support of communications among diverse, interested parties or stakeholders. This document is applicable to all types of organizations (e.g. commercial enterprises, government agencies, not-for-profit organizations).\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F89456.html","2026-01-27T15:12:13.073Z","2026-01-27T15:14:07.851Z","2026-01-27T15:14:07.914Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2815},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2816,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2820,2821,2827,2833,2839,2845,2851,2857],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2822,"documentId":2823,"title":2824,"createdAt":2825,"updatedAt":2825,"publishedAt":2826},674,"hzpf8a2qg2ga8hj4rxtwtqxb","Performance 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tasks","2026-01-26T23:39:33.050Z","2026-01-26T23:39:33.106Z",{"id":2602,"documentId":2603,"title":2604,"createdAt":2605,"updatedAt":2605,"publishedAt":2606},{"id":45,"documentId":2859,"number":2860,"title":2861,"part":11,"abstract":2862,"lastUpdatedYear":524,"linkToOfficial":2863,"createdAt":2864,"updatedAt":2865,"publishedAt":2866,"featured":11,"image":11,"development_stage":2867,"domain":2870,"issuing_body":2871,"ai_standard_keywords":2872},"xyculyhf0n1hfnjvdtcqfhxr","ISO\u002FIEC CD 4213","Artificial intelligence — Performance measurement for AI classification, regression, clustering and recommendation tasks","\u003Cp>This document specifies methodologies for measuring the performance of AI models for classification, regression, clustering and recommendation tasks.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F89455.html","2026-01-27T15:14:13.892Z","2026-01-27T15:15:24.423Z","2026-01-27T15:15:24.469Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2868},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2869,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":87,"documentId":88,"title":89,"createdAt":90,"updatedAt":90,"publishedAt":91},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2873,2874,2880,2886],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2875,"documentId":2876,"title":2877,"createdAt":2878,"updatedAt":2878,"publishedAt":2879},677,"kccjqinztgc5yh9flu3708pe","AI technologies","2026-01-26T23:39:33.054Z","2026-01-26T23:39:33.140Z",{"id":2881,"documentId":2882,"title":2883,"createdAt":2884,"updatedAt":2884,"publishedAt":2885},676,"zd1knm666msw8akrrc1scmvl","Application","2026-01-26T23:39:33.058Z","2026-01-26T23:39:33.109Z",{"id":2887,"documentId":2888,"title":2889,"createdAt":2890,"updatedAt":2890,"publishedAt":2891},682,"aaqwnuur5ac1twrk83bfgst7","Health informatics","2026-01-26T23:39:33.086Z","2026-01-26T23:39:33.203Z",{"id":2893,"documentId":2894,"number":2895,"title":2896,"part":11,"abstract":2183,"lastUpdatedYear":524,"linkToOfficial":2897,"createdAt":2898,"updatedAt":2899,"publishedAt":2900,"featured":11,"image":11,"development_stage":2901,"domain":2904,"issuing_body":2905,"ai_standard_keywords":2906},160,"j7m8dkm05niv1d1uw4abgfl0","ISO\u002FIEC CD TR 18988","Artificial intelligence — Application of AI technologies in health informatics","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F85602.html","2026-01-27T15:15:29.069Z","2026-01-27T15:17:08.306Z","2026-01-27T15:17:08.361Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2902},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2903,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2907,2908,2914,2920,2926,2927,2928,2934],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2909,"documentId":2910,"title":2911,"createdAt":2912,"updatedAt":2912,"publishedAt":2913},700,"gqvj5zclw94mrcquuuku9rcm","AI-enhanced nudging","2026-01-26T23:39:33.210Z","2026-01-26T23:39:33.363Z",{"id":2915,"documentId":2916,"title":2917,"createdAt":2918,"updatedAt":2918,"publishedAt":2919},692,"hbo74gther2z5etb61hmteft","Nudging mechanisms","2026-01-26T23:39:33.244Z","2026-01-26T23:39:33.351Z",{"id":2921,"documentId":2922,"title":2923,"createdAt":2924,"updatedAt":2924,"publishedAt":2925},693,"bqe0y9j4go9i0tsbgurv16bo","Definitions","2026-01-26T23:39:33.251Z","2026-01-26T23:39:33.354Z",{"id":142,"documentId":143,"title":144,"createdAt":145,"updatedAt":145,"publishedAt":146},{"id":265,"documentId":266,"title":267,"createdAt":268,"updatedAt":268,"publishedAt":269},{"id":2929,"documentId":2930,"title":2931,"createdAt":2932,"updatedAt":2932,"publishedAt":2933},695,"v5hjcomf2qldt8i652ssd323","Responsible AI","2026-01-26T23:39:33.277Z","2026-01-26T23:39:33.369Z",{"id":2935,"documentId":2936,"title":2937,"createdAt":2938,"updatedAt":2938,"publishedAt":2939},697,"k67n85ux4s71wdcyx5auhbep","Use-cases","2026-01-26T23:39:33.283Z","2026-01-26T23:39:33.371Z",{"id":2129,"documentId":2941,"number":2942,"title":2943,"part":11,"abstract":2944,"lastUpdatedYear":524,"linkToOfficial":2945,"createdAt":2946,"updatedAt":2947,"publishedAt":2948,"featured":11,"image":11,"development_stage":2949,"domain":2952,"issuing_body":2953,"ai_standard_keywords":2954},"dt1mxsdi924f9yzxl0z9v2rw","ISO\u002FIEC CD 25029","Artificial intelligence — AI-enhanced nudging","\u003Cp>This standard applies to nudging mechanisms enhanced by AI systems. This document provides definitions, concepts, and guidelines to address AI-enhanced nudging mechanisms by organisations. This standard aims to support organisations to deal with AI-enhanced nudging mechanisms in alignment with existing AI standards. “AI-enhanced nudging mechanisms” are a sub category of digital nudges and which are enhanced by AI systems. It provides use-cases to illustrate AI-enhanced nudging mechanisms. It provides guidelines and requirements for designing responsible AI-enhanced nudging mechanisms. This includes horizontal processes and key indicators using specific vertical examples.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F88817.html","2026-01-27T15:17:13.808Z","2026-01-27T15:19:01.234Z","2026-01-27T15:19:01.290Z",{"id":2122,"documentId":2123,"title":2124,"createdAt":2125,"updatedAt":2126,"publishedAt":2127,"icon":2950},{"id":2129,"documentId":2130,"name":2131,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2951,"hash":2133,"ext":79,"mime":80,"size":2134,"url":2135,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":2136,"updatedAt":2137,"publishedAt":2138},{},{"id":87,"documentId":88,"title":89,"createdAt":90,"updatedAt":90,"publishedAt":91},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2955,2956,2962,2963,2969,2975,2981],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":2957,"documentId":2958,"title":2959,"createdAt":2960,"updatedAt":2960,"publishedAt":2961},696,"m8vbk9kc929jgm4f76zrg6v9","Bias","2026-01-26T23:39:33.287Z","2026-01-26T23:39:33.373Z",{"id":2271,"documentId":2272,"title":2273,"createdAt":2274,"updatedAt":2274,"publishedAt":2275},{"id":2964,"documentId":2965,"title":2966,"createdAt":2967,"updatedAt":2967,"publishedAt":2968},698,"xp5i8vpeop9ydo6rka3lg1gk","AI aided decision making","2026-01-26T23:39:33.293Z","2026-01-26T23:39:33.377Z",{"id":2970,"documentId":2971,"title":2972,"createdAt":2973,"updatedAt":2973,"publishedAt":2974},699,"ywj88qlbhnm4ev1cr5xonf9m","Measurement techniques","2026-01-26T23:39:33.302Z","2026-01-26T23:39:33.385Z",{"id":2976,"documentId":2977,"title":2978,"createdAt":2979,"updatedAt":2979,"publishedAt":2980},701,"uuwokxtcc51tb5tjlw6bjey5","Bias treatment","2026-01-26T23:39:33.337Z","2026-01-26T23:39:33.402Z",{"id":2608,"documentId":2609,"title":2610,"createdAt":2611,"updatedAt":2611,"publishedAt":2612},{"id":2983,"documentId":2984,"number":2985,"title":2986,"part":11,"abstract":2987,"lastUpdatedYear":60,"linkToOfficial":2988,"createdAt":2989,"updatedAt":2990,"publishedAt":2991,"featured":11,"image":11,"development_stage":2992,"domain":2995,"issuing_body":2996,"ai_standard_keywords":2997},164,"p9elmxjgo5zv3qig9eam1zfd","ISO\u002FIEC TR 24027:2021","Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making","\u003Cp>This document addresses bias in relation to AI systems, especially with regards to AI-aided decision-making. Measurement techniques and methods for assessing bias are described, with the aim to address and treat bias-related vulnerabilities. All AI system lifecycle phases are in scope, including but not limited to data collection, training, continual learning, design, testing, evaluation and use.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F77607.html","2026-01-27T15:19:07.107Z","2026-01-27T15:22:45.901Z","2026-01-27T15:22:45.979Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":2993},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":2994,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[2998,2999,3005,3006,3007,3008,3014],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":3000,"documentId":3001,"title":3002,"createdAt":3003,"updatedAt":3003,"publishedAt":3004},710,"zaj4oxida949y4zmr6mz0v62","Ethical concerns","2026-01-26T23:39:33.390Z","2026-01-26T23:39:33.453Z",{"id":2437,"documentId":2438,"title":2439,"createdAt":2440,"updatedAt":2440,"publishedAt":2441},{"id":1892,"documentId":1893,"title":1894,"createdAt":1895,"updatedAt":1895,"publishedAt":1896},{"id":136,"documentId":137,"title":138,"createdAt":139,"updatedAt":139,"publishedAt":140},{"id":3009,"documentId":3010,"title":3011,"createdAt":3012,"updatedAt":3012,"publishedAt":3013},712,"zqyflvcfs0ckcxvhbk06kc0m","Processes","2026-01-26T23:39:33.428Z","2026-01-26T23:39:33.478Z",{"id":3015,"documentId":3016,"title":3017,"createdAt":3018,"updatedAt":3018,"publishedAt":3019},713,"k4ye6vntjwz1aw02auegog7q","International Standards","2026-01-26T23:39:33.431Z","2026-01-26T23:39:33.481Z",{"id":3021,"documentId":3022,"number":3023,"title":3024,"part":11,"abstract":3025,"lastUpdatedYear":171,"linkToOfficial":3026,"createdAt":3027,"updatedAt":3028,"publishedAt":3029,"featured":11,"image":11,"development_stage":3030,"domain":3033,"issuing_body":3034,"ai_standard_keywords":3035},166,"s8a15rmi3vprt37fklwztg5a","ISO\u002FIEC TR 24368:2022","Information technology — Artificial intelligence — Overview of ethical and societal concerns","\u003Cp>This document is a technical report that provides a high-level overview of the ethical and societal concerns surrounding artificial intelligence (AI). It outlines common sources of these concerns, highlights key principles and processes, and maps relevant international standards that support responsible AI development and use. The report is meant for technologists, regulators, interest groups, and society at large — without promoting any specific value system.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F78507.html","2026-01-27T15:22:52.061Z","2026-01-27T15:24:30.337Z","2026-01-27T15:24:30.409Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3031},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3032,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3036,3037,3038,3039,3040,3046,3052,3058],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":124,"documentId":125,"title":126,"createdAt":127,"updatedAt":127,"publishedAt":128},{"id":160,"documentId":161,"title":162,"createdAt":163,"updatedAt":163,"publishedAt":164},{"id":1911,"documentId":1912,"title":1913,"createdAt":1914,"updatedAt":1914,"publishedAt":1915},{"id":3041,"documentId":3042,"title":3043,"createdAt":3044,"updatedAt":3044,"publishedAt":3045},714,"tohni6ot3ci3hs3uho53b692","Best practices","2026-01-26T23:39:33.437Z","2026-01-26T23:39:33.491Z",{"id":3047,"documentId":3048,"title":3049,"createdAt":3050,"updatedAt":3050,"publishedAt":3051},716,"qpjcbcbzkt8gw1u04fcvvn6s","Risk assessment","2026-01-26T23:39:33.446Z","2026-01-26T23:39:33.506Z",{"id":3053,"documentId":3054,"title":3055,"createdAt":3056,"updatedAt":3056,"publishedAt":3057},720,"uqd9q4v6bzhu37hz1d24spog","Security controls","2026-01-26T23:39:33.448Z","2026-01-26T23:39:33.530Z",{"id":3059,"documentId":3060,"title":3061,"createdAt":3062,"updatedAt":3062,"publishedAt":3063},718,"gahjubzfyrwz1c3zcjmm7b43","Privacy controls","2026-01-26T23:39:33.458Z","2026-01-26T23:39:33.527Z",{"id":699,"documentId":3065,"number":3066,"title":3067,"part":11,"abstract":3068,"lastUpdatedYear":276,"linkToOfficial":3069,"createdAt":3070,"updatedAt":3071,"publishedAt":3072,"featured":11,"image":11,"development_stage":3073,"domain":3076,"issuing_body":3077,"ai_standard_keywords":3078},"twwuipvniz4zae1hpo6kbxqe","ISO\u002FIEC TR 27563:2023","Security and privacy in artificial intelligence use cases — Best practices","\u003Cp>This document outlines best practices on assessing security and privacy in artificial intelligence use cases, covering in particular those published in ISO\u002FIEC TR 24030. \u003Cbr>The following aspects are addressed: \u003Cbr>- an overall assessment of security and privacy on the AI system of interest,\u003Cbr>- security and privacy concerns, \u003Cbr>- security and privacy risks, \u003Cbr>- security and privacy controls, \u003Cbr>- security and privacy assurance and \u003Cbr>- security and privacy plans. \u003Cbr>Security and privacy are treated separately as the analysis of security and the analysis of privacy can differ.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F80396.html","2026-01-27T15:24:37.290Z","2026-01-27T15:26:28.414Z","2026-01-27T15:26:28.457Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3074},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3075,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3079,3080,3086,3092,3098,3104,3110,3116,3122,3123,3124],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":3081,"documentId":3082,"title":3083,"createdAt":3084,"updatedAt":3084,"publishedAt":3085},721,"iiyqq02brbyqp8aobh2jo2gv","Trustworthiness","2026-01-26T23:39:33.470Z","2026-01-26T23:39:33.558Z",{"id":3087,"documentId":3088,"title":3089,"createdAt":3090,"updatedAt":3090,"publishedAt":3091},717,"tralnlp0tvl980xmysgobnv0","Transparency","2026-01-26T23:39:33.462Z","2026-01-26T23:39:33.520Z",{"id":3093,"documentId":3094,"title":3095,"createdAt":3096,"updatedAt":3096,"publishedAt":3097},719,"uzq0uexorphi6ju6y111j20f","Explainability","2026-01-26T23:39:33.477Z","2026-01-26T23:39:33.546Z",{"id":3099,"documentId":3100,"title":3101,"createdAt":3102,"updatedAt":3102,"publishedAt":3103},722,"qb1utwflp2m6sfawwd89ocdq","Controllability","2026-01-26T23:39:33.525Z","2026-01-26T23:39:33.577Z",{"id":3105,"documentId":3106,"title":3107,"createdAt":3108,"updatedAt":3108,"publishedAt":3109},741,"uiipi7y9jsaihm7aut1kgoeb","Threats","2026-01-26T23:39:33.769Z","2026-01-26T23:39:33.850Z",{"id":3111,"documentId":3112,"title":3113,"createdAt":3114,"updatedAt":3114,"publishedAt":3115},733,"y6jamci9wiq5b45rkxemrg4u","Risks","2026-01-26T23:39:33.753Z","2026-01-26T23:39:33.813Z",{"id":3117,"documentId":3118,"title":3119,"createdAt":3120,"updatedAt":3120,"publishedAt":3121},734,"dkf0h8pjhzmmrnyvukg2x40o","Mitigation","2026-01-26T23:39:33.755Z","2026-01-26T23:39:33.815Z",{"id":112,"documentId":113,"title":114,"createdAt":115,"updatedAt":115,"publishedAt":116},{"id":124,"documentId":125,"title":126,"createdAt":127,"updatedAt":127,"publishedAt":128},{"id":160,"documentId":161,"title":162,"createdAt":163,"updatedAt":163,"publishedAt":164},{"id":717,"documentId":3126,"number":3127,"title":3128,"part":11,"abstract":3129,"lastUpdatedYear":958,"linkToOfficial":3130,"createdAt":3131,"updatedAt":3132,"publishedAt":3133,"featured":11,"image":11,"development_stage":3134,"domain":3137,"issuing_body":3138,"ai_standard_keywords":3139},"m6bq11erkmr9bag90x5impg4","ISO\u002FIEC TR 24028:2020","Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence","\u003Cp>This document surveys topics related to trustworthiness in AI systems, including the following:\u003Cbr>- approaches to establish trust in AI systems through transparency, explainability, controllability, etc.; \u003Cbr>- engineering pitfalls and typical associated threats and risks to AI systems, along with possible mitigation techniques and methods; and \u003Cbr>- approaches to assess and achieve availability, resiliency, reliability, accuracy, safety, security and privacy of AI systems. \u003Cbr>The specification of levels of trustworthiness for AI systems is out of the scope of this document.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F77608.html","2026-01-27T15:26:33.941Z","2026-01-27T15:28:12.107Z","2026-01-27T15:28:12.155Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3135},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3136,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3140,3141,3147,3153,3159,3165,3171,3177],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":3142,"documentId":3143,"title":3144,"createdAt":3145,"updatedAt":3145,"publishedAt":3146},735,"h11a3yl7i2xr6rvug0ldqvp6","Beneficial AI systems","2026-01-26T23:39:33.758Z","2026-01-26T23:39:33.818Z",{"id":3148,"documentId":3149,"title":3150,"createdAt":3151,"updatedAt":3151,"publishedAt":3152},736,"c9ad1aqwd2rgljer0y03fdap","Functional benefits","2026-01-26T23:39:33.760Z","2026-01-26T23:39:33.820Z",{"id":3154,"documentId":3155,"title":3156,"createdAt":3157,"updatedAt":3157,"publishedAt":3158},737,"yyy2hcdnz3ki60cca439a7wx","Economic benefits","2026-01-26T23:39:33.762Z","2026-01-26T23:39:33.822Z",{"id":3160,"documentId":3161,"title":3162,"createdAt":3163,"updatedAt":3163,"publishedAt":3164},738,"jzwve74r0ip8k7piv2345sus","Environmental benefits","2026-01-26T23:39:33.764Z","2026-01-26T23:39:33.824Z",{"id":3166,"documentId":3167,"title":3168,"createdAt":3169,"updatedAt":3169,"publishedAt":3170},739,"sqb5yhb17imre7f320l01m56","Societal benefits","2026-01-26T23:39:33.767Z","2026-01-26T23:39:33.827Z",{"id":3172,"documentId":3173,"title":3174,"createdAt":3175,"updatedAt":3175,"publishedAt":3176},413,"v8n8y3lbtn66m7hl7l38p074","Stakeholders","2026-01-26T23:39:29.382Z","2026-01-26T23:39:29.468Z",{"id":1911,"documentId":1912,"title":1913,"createdAt":1914,"updatedAt":1914,"publishedAt":1915},{"id":745,"documentId":3179,"number":3180,"title":3181,"part":11,"abstract":3182,"lastUpdatedYear":524,"linkToOfficial":3183,"createdAt":3184,"updatedAt":3185,"publishedAt":3186,"featured":11,"image":11,"development_stage":3187,"domain":3190,"issuing_body":3191,"ai_standard_keywords":3192},"suq6edlm0oea1kzhdiv3fu7i","ISO\u002FIEC TR 21221:2025","Information technology – Artificial intelligence – Beneficial AI systems","\u003Cp>This document describes the delivery of functional, economic, environmental, social, societal, cultural, intellectual and personal benefits by AI systems as perceived by their stakeholders. The document includes illustrative use cases of AI systems.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F86690.html","2026-01-27T15:28:17.520Z","2026-01-27T15:29:42.086Z","2026-01-27T15:29:42.127Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3188},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3189,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3193,3194,3200,3201,3202,3203],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":3195,"documentId":3196,"title":3197,"createdAt":3198,"updatedAt":3198,"publishedAt":3199},746,"e7vy2aiwtypyalvbzg08a79x","Machine learning (ML) computing devices","2026-01-26T23:39:33.778Z","2026-01-26T23:39:33.867Z",{"id":1892,"documentId":1893,"title":1894,"createdAt":1895,"updatedAt":1895,"publishedAt":1896},{"id":148,"documentId":149,"title":150,"createdAt":151,"updatedAt":151,"publishedAt":152},{"id":2095,"documentId":2096,"title":2097,"createdAt":2098,"updatedAt":2098,"publishedAt":2099},{"id":2414,"documentId":2415,"title":2416,"createdAt":2417,"updatedAt":2417,"publishedAt":2418},{"id":753,"documentId":3205,"number":3206,"title":3207,"part":11,"abstract":3208,"lastUpdatedYear":1415,"linkToOfficial":3209,"createdAt":3210,"updatedAt":3211,"publishedAt":3212,"featured":11,"image":11,"development_stage":3213,"domain":3216,"issuing_body":3217,"ai_standard_keywords":3218},"chopt2wdnrt3k3ozhhhnmppt","ISO\u002FIEC TR 17903:2024","Information technology – Artificial intelligence – Overview of machine learning computing devices","\u003Cp>This document surveys machine learning (ML) computing devices, including the following: \u003Cbr>- ML computing device terminology and characteristics; \u003Cbr>- existing approaches to the setting and use of characteristics for optimizing ML computing device performance. \u003Cbr>The information provided in this document is relevant for organizations of all types and sizes.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F85078.html","2026-01-27T15:29:47.236Z","2026-01-27T15:31:23.268Z","2026-01-27T15:31:23.316Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3214},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3215,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3219,3220,3221,3222,3223,3224,3225,3226],{"id":1616,"documentId":1617,"title":1618,"createdAt":1619,"updatedAt":1619,"publishedAt":1620},{"id":124,"documentId":125,"title":126,"createdAt":127,"updatedAt":127,"publishedAt":128},{"id":160,"documentId":161,"title":162,"createdAt":163,"updatedAt":163,"publishedAt":164},{"id":1911,"documentId":1912,"title":1913,"createdAt":1914,"updatedAt":1914,"publishedAt":1915},{"id":3041,"documentId":3042,"title":3043,"createdAt":3044,"updatedAt":3044,"publishedAt":3045},{"id":3047,"documentId":3048,"title":3049,"createdAt":3050,"updatedAt":3050,"publishedAt":3051},{"id":3053,"documentId":3054,"title":3055,"createdAt":3056,"updatedAt":3056,"publishedAt":3057},{"id":3059,"documentId":3060,"title":3061,"createdAt":3062,"updatedAt":3062,"publishedAt":3063},{"id":772,"documentId":3228,"number":3229,"title":3230,"part":11,"abstract":3231,"lastUpdatedYear":60,"linkToOfficial":3232,"createdAt":3233,"updatedAt":3234,"publishedAt":3235,"featured":11,"image":11,"development_stage":3236,"domain":3239,"issuing_body":3240,"ai_standard_keywords":3241},"t8lyn4qv77550d4picrfe53q","ISO\u002FIEC TR 24372:2021","Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems","\u003Cp>This document provides an overview of the state of the art of computational approaches for AI systems, by describing: a) main computational characteristics of AI systems; b) main algorithms and approaches used in AI systems, referencing use cases contained in ISO\u002FIEC TR 24030.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F78508.html","2026-01-27T15:31:27.634Z","2026-01-27T15:33:03.021Z","2026-01-27T15:33:03.093Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3237},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3238,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3242,3248,3249,3255,3261,3267],{"id":3243,"documentId":3244,"title":3245,"createdAt":3246,"updatedAt":3246,"publishedAt":3247},740,"z66qfppdspa1quk82nvmgl49","Computational approaches","2026-01-26T23:39:33.776Z","2026-01-26T23:39:33.840Z",{"id":2271,"documentId":2272,"title":2273,"createdAt":2274,"updatedAt":2274,"publishedAt":2275},{"id":3250,"documentId":3251,"title":3252,"createdAt":3253,"updatedAt":3253,"publishedAt":3254},752,"q5m75ead7ji5nbq8tebjxaq5","Computational characteristics","2026-01-26T23:39:33.880Z","2026-01-26T23:39:33.927Z",{"id":3256,"documentId":3257,"title":3258,"createdAt":3259,"updatedAt":3259,"publishedAt":3260},753,"e7tqy9wt0bbdmdo1tz9zchf9","Algorithms","2026-01-26T23:39:33.882Z","2026-01-26T23:39:33.929Z",{"id":3262,"documentId":3263,"title":3264,"createdAt":3265,"updatedAt":3265,"publishedAt":3266},754,"js2w97mzv6r4f3p7dm9e1kxv","Approaches","2026-01-26T23:39:33.884Z","2026-01-26T23:39:33.932Z",{"id":1911,"documentId":1912,"title":1913,"createdAt":1914,"updatedAt":1914,"publishedAt":1915},{"id":3269,"documentId":3270,"number":3271,"title":3272,"part":3273,"abstract":3274,"lastUpdatedYear":958,"linkToOfficial":3275,"createdAt":3276,"updatedAt":3277,"publishedAt":3278,"featured":11,"image":11,"development_stage":3279,"domain":3282,"issuing_body":3283,"ai_standard_keywords":3284},178,"xxxthqc5lcabanud3eh80088","ISO\u002FIEC TR 29119-11:2020","Software and systems engineering — Software testing ","Part 11: Guidelines on the testing of AI-based systems","\u003Cp>This document provides an introduction to AI-based systems. These systems are typically complex (e.g. deep neural nets), are sometimes based on big data, can be poorly specified and can be non-deterministic, which creates new challenges and opportunities for testing them.\u003C\u002Fp>\u003Cp>This document explains those characteristics which are specific to AI-based systems and explains the corresponding difficulties of specifying the acceptance criteria for such systems. z\u003C\u002Fp>\u003Cp>This document presents the challenges of testing AI-based systems, the main challenge being the test oracle problem, whereby testers find it difficult to determine expected results for testing and therefore whether tests have passed or failed. It covers testing of these systems across the life cycle and gives guidelines on how AI-based systems in general can be tested using black-box approaches and introduces white-box testing specifically for neural networks. It describes options for the test environments and test scenarios used for testing AI-based systems. In this document an AI-based system is a system that includes at least one AI component.\u003C\u002Fp>","https:\u002F\u002Fwww.iso.org\u002Fstandard\u002F79016.html","2026-01-27T15:33:08.544Z","2026-01-27T15:35:35.891Z","2026-01-27T15:35:35.968Z",{"id":66,"documentId":67,"title":68,"createdAt":69,"updatedAt":70,"publishedAt":71,"icon":3280},{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":3281,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":1711,"documentId":1712,"title":1713,"createdAt":1714,"updatedAt":1714,"publishedAt":1715},{"id":539,"documentId":540,"title":541,"createdAt":542,"updatedAt":542,"publishedAt":543},[3285,3291,3292,3298,3304,3310,3316,3322,3323,3329,3335,3336],{"id":3286,"documentId":3287,"title":3288,"createdAt":3289,"updatedAt":3289,"publishedAt":3290},755,"zxsmljr8eya5s0rvga4rojx0","AI-based systems","2026-01-26T23:39:33.886Z","2026-01-26T23:39:33.935Z",{"id":2577,"documentId":2578,"title":2579,"createdAt":2580,"updatedAt":2580,"publishedAt":2581},{"id":3293,"documentId":3294,"title":3295,"createdAt":3296,"updatedAt":3296,"publishedAt":3297},756,"xfa95jjzoetcmg2jk9n6q37m","Deep neural nets","2026-01-26T23:39:33.888Z","2026-01-26T23:39:33.939Z",{"id":3299,"documentId":3300,"title":3301,"createdAt":3302,"updatedAt":3302,"publishedAt":3303},758,"f3hn9c9o8lkrkspn6ewze8tw","Big data","2026-01-26T23:39:33.894Z","2026-01-26T23:39:33.965Z",{"id":3305,"documentId":3306,"title":3307,"createdAt":3308,"updatedAt":3308,"publishedAt":3309},768,"e2cdxrjskmomzwg84v46h1u3","Non-deterministic systems","2026-01-26T23:39:33.907Z","2026-01-26T23:39:34.051Z",{"id":3311,"documentId":3312,"title":3313,"createdAt":3314,"updatedAt":3314,"publishedAt":3315},759,"hkronalqgbefw7u3m74a7a4w","Test oracle problem","2026-01-26T23:39:33.905Z","2026-01-26T23:39:33.982Z",{"id":3317,"documentId":3318,"title":3319,"createdAt":3320,"updatedAt":3320,"publishedAt":3321},765,"ko4iyycd5obvv568q5wgnxza","Acceptance criteria","2026-01-26T23:39:33.924Z","2026-01-26T23:39:34.011Z",{"id":130,"documentId":131,"title":132,"createdAt":133,"updatedAt":133,"publishedAt":134},{"id":3324,"documentId":3325,"title":3326,"createdAt":3327,"updatedAt":3327,"publishedAt":3328},770,"uoydabq1w90pnda7u85d04jv","Black-box approaches","2026-01-26T23:39:33.989Z","2026-01-26T23:39:34.095Z",{"id":3330,"documentId":3331,"title":3332,"createdAt":3333,"updatedAt":3333,"publishedAt":3334},771,"nehqtf3mt8gk2as5snq6x6b7","White-box testing","2026-01-26T23:39:34.031Z","2026-01-26T23:39:34.116Z",{"id":1785,"documentId":1786,"title":1787,"createdAt":1788,"updatedAt":1788,"publishedAt":1789},{"id":3337,"documentId":3338,"title":3339,"createdAt":3340,"updatedAt":3340,"publishedAt":3341},772,"hz0z0x411wl5nctnb71azd7j","AI component","2026-01-26T23:39:34.035Z","2026-01-26T23:39:34.119Z",{"id":3343,"documentId":3344,"number":3345,"title":3346,"part":3347,"abstract":3348,"lastUpdatedYear":524,"linkToOfficial":3349,"createdAt":3350,"updatedAt":3351,"publishedAt":3352,"featured":11,"image":11,"development_stage":3353,"domain":3356,"issuing_body":3357,"ai_standard_keywords":3358},180,"m5zhznla2kju77u8sevhjo9k","ISO\u002FIEC TS 42119-2025","Artificial intelligence — Testing of AI  ","Part 2: Overview of testing AI systems","\u003Cp>This document provides requirements and guidance on the application of the ISO\u002FIEC\u002FIEEE 29119 series to the testing of AI systems. This document follows a risk-based approach and uses risks associated with AI systems, and their development and maintenance, to identify suitable test practices, approaches and techniques applicable to AI systems and their components. 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These assessments focus on understanding how AI systems — and their foreseeable applications — may affect individuals, groups, or society at large. 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This document also provides guidance to a wider community, including executive managers, external businesses or technical specialists, such as legal or accounting specialists, retail or industrial associations, or professional bodies, public authorities and policymakers, internal and external service providers (including consultants), assessors and auditors.This document is applicable to the governance of current and future uses of AI as well as the implications of such use for the organization itself. This document is applicable to any organization, including public and private companies, government entities and not-for-profit organizations. This document is applicable to an organization of any size irrespective of their dependence on data or information 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8663:2025","Information technology — Brain-computer interfaces — Vocabulary","\u003Cp>This document specifies the terms and definitions commonly used in the field of brain–computer interface (BCI), including basic concepts and classifications of BCI, hardware, experiment setups and protocols used in BCI, related neuroscience concepts of BCI (e.g. coding and decoding, feedback and stimulation), and its 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This document is applicable to all types of organizations (e.g. commercial enterprises, government agencies, not-for-profit 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7012-2025","Approved Draft Standard for Machine Readable Personal Privacy Terms","\u003Cp>This draft standard covers contractual interactions and agreements between individuals and the service providers they engage on a network, including websites. It describes how individuals, acting as first parties, can proffer their privacy requirements as contractual terms and arrive at agreements recorded and kept by both sides. These terms shall be chosen from a collection of standard-form agreements in a roster kept by an independent and neutral non-business entity. Computing devices and software performing as agents for both first and second parties shall engage using any protocol that serves the purpose. The first party shall point to a preferred agreement, or a set of agreements, from which the second party shall accept one. Party-to-party negotiations over terms in any of these contracts or other agreements are outside the scope of this standard. If both parties agree, the chosen contract or agreement shall be signed electronically by both parties or their agents, and a matching record shall be kept by both sides in a form that can be retrieved, audited, or disputed, if necessary, at some later time—and which is available to do so easily.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7012\u002F7192\u002F","2026-01-27T15:48:00.931Z","2026-01-27T15:49:59.020Z","2026-01-27T15:49:59.071Z",{"id":4149,"documentId":4150,"number":4151,"title":4152,"part":11,"abstract":4153,"lastUpdatedYear":171,"linkToOfficial":4154,"createdAt":4155,"updatedAt":4156,"publishedAt":4157,"featured":11},197,"m0bncv85txie3r7kqij55wfs","IEEE P3193","Recommended Practice on Large-scale Pre-trained Deep Learning Model Application Framework","\u003Cp>This recommended practice provides a framework for the use of large-scale pre-trained deep learning models, including approaches, taxonomies, related roles, and activities, software toolkits, application ability key performance indicators as well as assessment means.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3193\u002F11014\u002F","2026-01-27T15:50:13.623Z","2026-01-27T15:54:53.816Z","2026-01-27T15:54:53.877Z",{"id":4159,"documentId":4160,"number":4161,"title":4162,"part":11,"abstract":4163,"lastUpdatedYear":276,"linkToOfficial":4164,"createdAt":4165,"updatedAt":4166,"publishedAt":4167,"featured":11},199,"yk9poxln4k7k9pb2vx6elwxh","IEEE P7018","Standard for Security and Trustworthiness Requirements in Generative Pretrained Artificial Intelligence (AI) Models","\u003Cp>This standard establishes a comprehensive framework for mitigating security risks, privacy leaking in the development, deployment, and use of generative pretrained AI models. The framework encompasses requirements concerning security risks, functional and non-functional requirements, and evaluation metrics such as transparency, interpretability, and explainability.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7018\u002F11306\u002F","2026-01-27T15:55:07.595Z","2026-01-27T15:57:07.613Z","2026-01-27T15:57:07.670Z",{"id":4169,"documentId":4170,"number":4171,"title":4172,"part":11,"abstract":4173,"lastUpdatedYear":1415,"linkToOfficial":4174,"createdAt":4175,"updatedAt":4176,"publishedAt":4177,"featured":11},201,"i9et2hy7nxuz6vx5aq28h4yc","IEEE P3447","System Architecture and General Requirements for Pre-trained Large Language Models (LLM) Applications in the Smart Home Industry","\u003Cp>This standard provides definitions, terms, frameworks, and general requirements for systems that apply pre-trained large language models (LLM) in the smart home industry. It covers foundational platforms, model capabilities, scenario applications, and safety requirements.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3447\u002F11519\u002F","2026-01-27T15:57:31.855Z","2026-01-27T15:59:11.393Z","2026-01-27T15:59:11.464Z",{"id":4179,"documentId":4180,"number":4181,"title":4182,"part":11,"abstract":4183,"lastUpdatedYear":524,"linkToOfficial":4184,"createdAt":4185,"updatedAt":4186,"publishedAt":4187,"featured":11},203,"gp461ff2ag7imadbkom18ppz","IEEE P3458","IEEE Draft Standard for Domain-Specific Large Language Model Management Platforms","\u003Cp>This standard specifies a domain-specific architecture of a management platform for Large Language Models (LLMs). The standard includes requirements regarding the deployment, security, management, and integration of these platforms. The standard also includes service guidelines and evaluation indexes for domain-specific LLMs management platforms.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3458\u002F12136\u002F","2026-01-27T15:59:21.626Z","2026-01-27T16:01:38.759Z","2026-01-27T16:01:38.873Z",{"id":4189,"documentId":4190,"number":4191,"title":4192,"part":11,"abstract":4193,"lastUpdatedYear":1415,"linkToOfficial":4194,"createdAt":4195,"updatedAt":4196,"publishedAt":4197,"featured":11},205,"yqfovmh3iwh8qr3d4vvv9hi4","IEEE P3462","Recommended Practice for Using Safety by Design in Generative Models to Prioritize Child Safety","\u003Cp>This recommended practice follows a safety by design approach by providing recommendations for developing, deploying, and maintaining generative artificial intelligence models with adequate safeguards against child sexual abuse. The document provides a framework that expands child safety to the entire lifecycle of machine learning (ML): development, deployment, and maintenance stages. Each part in the process includes opportunities to prioritize child safety, regardless of data modality (i.e. text, image, video, audio) or whether an organization releases its technology as closed source or open source, or some release option between these two.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3462\u002F11584\u002F","2026-01-27T16:01:44.638Z","2026-01-27T16:03:22.855Z","2026-01-27T16:03:22.915Z",{"id":872,"documentId":4199,"number":4200,"title":4201,"part":11,"abstract":4202,"lastUpdatedYear":276,"linkToOfficial":4203,"createdAt":4204,"updatedAt":4205,"publishedAt":4206,"featured":11},"ou1ojalbt2bdyshmz6o5mpi5","IEEE P3430","Recommended Practice for A Holistic Framework for AI Foundation Models","\u003Cp>This recommended practice provides a holistic framework for AI foundation models. The framework allows sharing of the common computing resources, data and infrastructure in the usage(s) and application(s) of AI Foundation models. The detailed technical requirements for the holistic framework are provided. Typical use cases illustrate the use of the holistic framework for AI foundation models.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3430\u002F11491\u002F","2026-01-27T16:03:37.736Z","2026-01-27T16:05:24.992Z","2026-01-27T16:05:25.051Z",{"id":906,"documentId":4208,"number":4209,"title":4210,"part":11,"abstract":4211,"lastUpdatedYear":276,"linkToOfficial":4212,"createdAt":4213,"updatedAt":4214,"publishedAt":4215,"featured":11},"p4liwjk2rc7dhue5t52s75xb","IEEE P3428","Standard for Large Language Model Agents for AI-powered Education","\u003Cp>This standard provides: 1. Agent Components: The building blocks and architectural elements that constitute an educational Large Language Model (LLM) agent. 2. Agent Interoperability Protocols: A communication protocol for interaction between different LLM agents and other components in the educational ecosystem. 3. Agent Life Cycle and States: Definitions of and standardized stages of deployment, operation, and retirement of educational LLM agents. 4. Foundation Models and LLM Embedding Mechanisms: Guidelines for embedding and customizing foundation models within educational environments. 5. Evaluation of Education LLM Agents: Metrics and benchmarks for assessing the effectiveness, efficiency, and ethical considerations of LLM agents in educational settings.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3428\u002F11489\u002F","2026-01-27T16:05:31.358Z","2026-01-27T16:07:22.925Z","2026-01-27T16:07:22.975Z",{"id":918,"documentId":4217,"number":4218,"title":4219,"part":11,"abstract":4220,"lastUpdatedYear":276,"linkToOfficial":4221,"createdAt":4222,"updatedAt":4223,"publishedAt":4224,"featured":11},"rvf2pt58f929lq9a7341sfl5","IEEE P3427","Standard for Federated Machine Learning of Semantic Information Agents","\u003Cp>This standard defines a comprehensive framework for federated machine learning of semantic information agents. It targets two primary layers: • Information Protocol Layer: This layer focuses on binary compression and protocol-level data structures for federated information exchange, with an emphasis on efficiency and security. • Semantic Layer: This layer focuses on the meaning and interpretation of information through domain-specific languages (DSLs) that are human-understandable. The standard also provides rule-based guidance and optimization strategies for different nodes within federated learning networks, addressing role definitions for agents, reward mechanisms, and multi-agent system regulations.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3427\u002F11488\u002F","2026-01-27T16:07:28.532Z","2026-01-27T16:09:24.825Z","2026-01-27T16:09:24.868Z",{"id":930,"documentId":4226,"number":4227,"title":4228,"part":11,"abstract":4229,"lastUpdatedYear":276,"linkToOfficial":4230,"createdAt":4231,"updatedAt":4232,"publishedAt":4233,"featured":11},"tdnn9x3nhye1cmq47x9r97vj","IEEE P3423","Standard for End-to-end Framework Design for Autonomous Driving","\u003Cp>This standard addresses the evaluation of safety, explainability, and stability of algorithms implementing autonomous driving levels, as defined by SAE J3016. The standard specifies the minimum requirements for experimental setups, data flow charts, algorithm pipelines, and automated quantitative evaluation metrics for autonomous driving algorithms that use available Artificial Intelligence (AI) foundation models. Because of the rapid pace of commercialization of large AI foundation models, this standard specifies a software escrow service for proprietary code and automated processes for algorithms that use AI foundation models in the driving scenario.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3423\u002F11437\u002F","2026-01-27T16:09:36.844Z","2026-01-27T16:11:20.936Z","2026-01-27T16:11:20.985Z",{"id":4235,"documentId":4236,"number":4237,"title":4238,"part":11,"abstract":4239,"lastUpdatedYear":276,"linkToOfficial":4240,"createdAt":4241,"updatedAt":4242,"publishedAt":4243,"featured":11},215,"n36gj4wf9cwa9abec592b3rx","IEEE P3426","Standard for Defining and Measuring the Capabilities of AI Foundation Models","\u003Cp>This standard defines and provides criteria to measure the capabilities of foundation models. The standard focuses on measurable and objective capabilities such as perception-oriented capabilities, cognitive capabilities, and learning capabilities. The standard provides guidelines for evaluating these capabilities, drawing inspiration from human intelligence quotient (IQ) definition and measurement, and includes: - Definition of key capabilities pertinent to foundation models. - Standardized methods and metrics for evaluating perception-oriented, cognitive, and learning capabilities. - Framework for comparing different models based on their cognitive capabilities. - Ethical considerations and potential biases in the evaluation process.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3426\u002F11487\u002F","2026-01-27T16:11:29.952Z","2026-01-27T16:13:38.785Z","2026-01-27T16:13:38.836Z",{"id":4245,"documentId":4246,"number":4247,"title":4248,"part":11,"abstract":4249,"lastUpdatedYear":276,"linkToOfficial":4250,"createdAt":4251,"updatedAt":4252,"publishedAt":4253,"featured":11},217,"brr9i6p43rdbytxo03xiyqku","IEEE P3429","Recommended Practices for Levels of Artificial Intelligence Generated Content Technologies","\u003Cp>This recommended practice provides an overview of Artificial Intelligence Generated Content (AIGC) technologies, levels of those AICG technologies and defines the following: • Recommended rules for levels of AIGC technologies; • A detailed recommended practice for levels of AIGC technologies; • Use cases for the practical usage of levels of AIGC technologies.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3429\u002F11490\u002F","2026-01-27T16:13:44.359Z","2026-01-27T16:15:08.385Z","2026-01-27T16:15:08.450Z",{"id":948,"documentId":4255,"number":4256,"title":4257,"part":11,"abstract":4258,"lastUpdatedYear":276,"linkToOfficial":4259,"createdAt":4260,"updatedAt":4261,"publishedAt":4262,"featured":11},"i961t7qi0jusikqpok657tas","IEEE P3395","Standard for the Implementation of Safeguards, Controls, and Preventive Techniques for Artificial Intelligence (AI) Models","\u003Cp>This standard addresses Artificial Intelligence (AI) risks such as malicious use, AI race, organizational risks, and rogue AI's. The standard defines the relevant terminology, potential applications, and a risk abatement strategy for the behavior of AI models. The standard does not detail these foundational applications, but describes them in general.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3395\u002F11378\u002F","2026-01-27T16:15:17.003Z","2026-01-27T16:16:54.365Z","2026-01-27T16:16:54.409Z",{"id":977,"documentId":4264,"number":4265,"title":4266,"part":11,"abstract":4267,"lastUpdatedYear":276,"linkToOfficial":4268,"createdAt":4269,"updatedAt":4270,"publishedAt":4271,"featured":11},"gw7kpz2ve96tjskge08piv0w","IEEE P3419","Standard for Large Language Model Evaluation","\u003Cp>This standard establishes a comprehensive set of criteria for the evaluation of Large Language Models (LLMs) and extends to multimodal models. Primarily, the standard provides a multi-dimensional evaluation framework for the users, teams, and enterprises who evaluate or use LLM technology or models. The standard describes a three-dimensional evaluation framework anchored in the principles of “versatility, intelligence, efficiency, and safety.” This framework is categorized under \"capability-task-metrics\" and encompasses evaluations at different stages, including foundation models, pre-training algorithms, and fine-tuning algorithms. The standard provides objective and subjective viewpoints and offers an extensive understanding of LLMs' multifaceted capabilities and potentialities. This comprehensive understanding greatly assists researchers and developers, as well as downstream users. Furthermore, the open-source evaluation platform operations embedded in the framework help stimulating innovation in model and algorithm research and drive forward their industrial applications.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3419\u002F11384\u002F","2026-01-27T16:17:11.771Z","2026-01-27T16:19:17.563Z","2026-01-27T16:19:17.617Z",{"id":4273,"documentId":4274,"number":4275,"title":4276,"part":11,"abstract":4277,"lastUpdatedYear":276,"linkToOfficial":4278,"createdAt":4279,"updatedAt":4280,"publishedAt":4281,"featured":11},223,"yc5j5le9disqdqxv7mptenuz","IEEE P3403","Recommended Practice on Data Processing for Training Large Language Models","\u003Cp>This recommended practice provides a data processing framework for training large language models, refining the relevant terms and definitions. The recommended practice also provides data processing processes, methods, characteristics and performance evaluation during the pre-training and fine-tuning stages.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3403\u002F11381\u002F","2026-01-27T16:19:22.791Z","2026-01-27T16:20:59.982Z","2026-01-27T16:21:00.038Z",{"id":4283,"documentId":4284,"number":4285,"title":4286,"part":11,"abstract":4287,"lastUpdatedYear":276,"linkToOfficial":4288,"createdAt":4289,"updatedAt":4290,"publishedAt":4291,"featured":11},225,"d8v1wwornglkwk5j9eqrwdef","IEEE P3398","Recommended Practice for Generative Pre-trained Transformer Empowered Software Engineering Life Cycle","\u003Cp>For the software engineering life cycle empowered by Generative Pre-trained Transformer (GPT) this recommended practice specifies: • a description and definitions, • the competency in each stage applying GPT, • levels of intelligence empowered by GPT, • intelligence level evaluation methods. A generative pre-trained transformer (GPT) is a type of neural network architecture used in natural language processing (NLP) applications. GPTs are transformational self-supervised NLP models that can generate human-like language and fine-tune to various downstream tasks, enabled by their pre-training approach and large transformer architectures.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3398\u002F11380\u002F","2026-01-27T16:21:05.045Z","2026-01-27T16:22:57.167Z","2026-01-27T16:22:57.204Z",{"id":4293,"documentId":4294,"number":4295,"title":4296,"part":11,"abstract":4297,"lastUpdatedYear":276,"linkToOfficial":4298,"createdAt":4299,"updatedAt":4300,"publishedAt":4301,"featured":11},227,"ryjuk8ri9a9a9hg2o2c82k35","IEEE P3396","Recommended Practice for Defining and Evaluating Artificial Intelligence (AI) Risk, Safety, Trustworthiness, and Responsibility","\u003Cp>This recommended practice provides a comprehensive framework for understanding, defining, and evaluating AI risks, AI safety, AI trustworthiness, and AI responsibility in order to address and manage them while preserving the benefits of innovation. This recommended practice takes into consideration the global context, advocating for responsible AI adoption, governance, and collaboration. 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7002-2022","IEEE Standard for Data Privacy Process","\u003Cp>The requirements for a systems\u002Fsoftware engineering process for privacy-oriented considerations regarding products, services, and systems utilizing employee, customer, or other external user’s personal data are defined by this standard. Organizations and projects that are developing and deploying products, systems, processes, and applications that involve personal information are candidate users of the IEEE Std 7002™ standard. Specific procedures, diagrams, and checklists are provided for users of the IEEE Std 7002 standard to perform conformity assessments on their specific privacy practices. Privacy impact assessments (PIAs) are described as a tool for both identifying where privacy controls and measures are needed and for confirming they are in place.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7002\u002F6898\u002F","2026-01-27T15:41:19.280Z","2026-01-27T15:43:51.476Z","2026-01-27T15:43:51.542Z",{"id":794,"documentId":4476,"number":4477,"title":4478,"part":11,"abstract":4479,"lastUpdatedYear":524,"linkToOfficial":4480,"createdAt":4481,"updatedAt":4482,"publishedAt":4483,"featured":11},"dwn9fyfqgv318q71f25kfzp3","IEEE 7003-2024","IEEE Standard for Algorithmic Bias Considerations","\u003Cp>The processes and methodologies to help users address issues of bias in the creation of algorithms are described in this standard. Elements include but are not limited to: criteria for the selection of validation data sets for bias quality control, guidelines on establishing and communicating the application boundaries for which the algorithm has been designed and validated to guard against unintended consequences arising from out-of-bound application of algorithms, and suggestions for user expectation management to help mitigate bias due to incorrect interpretation of systems outputs by users (e.g., correlation vs. causation).\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7003\u002F11357\u002F","2026-01-27T15:43:57.109Z","2026-01-27T15:45:37.999Z","2026-01-27T15:45:38.048Z",{"id":818,"documentId":4485,"number":4486,"title":4487,"part":11,"abstract":4488,"lastUpdatedYear":958,"linkToOfficial":4489,"createdAt":4490,"updatedAt":4491,"publishedAt":4492,"featured":11},"jibqq3naii1m42r3o3l3vfn4","IEEE 7010-2020","IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being","\u003Cp>The impact of artificial intelligence or autonomous and intelligent systems (A\u002FIS) on humans is measured by this standard. The positive outcome of A\u002FIS on human well-being is the overall intent of this standard. Scientifically valid well-being indices currently in use and based on a stakeholder engagement process ground this standard. Product development guidance, identification of areas for improvement, risk management, performance assessment, and the identification of intended and unintended users, uses and impacts on human well-being of A\u002FIS are the intents of this standard.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7010\u002F7718\u002F","2026-01-27T15:45:49.510Z","2026-01-27T15:47:55.150Z","2026-01-27T15:47:55.195Z",{"id":806,"documentId":4494,"number":4495,"title":4496,"part":11,"abstract":4497,"lastUpdatedYear":1415,"linkToOfficial":4498,"createdAt":4499,"updatedAt":4500,"publishedAt":4501,"featured":11},"uieivnl571u1vs0mp9fwc7s3","IEEE 3187-2024","IEEE Guide for Framework for Trustworthy Federated Machine Learning","\u003Cp>The development and application of federated machine learning are facing the critical challenges of balancing the tradeoff among privacy, security, performance, and efficiency, how to realize supervision covering the whole life cycle, and how to get explainable results. Thus, trustworthy federated machine learning is proposed to solve the above problem. In this standard, a general view of framework for trustworthy federated machine learning is provided in four parts: a principle in trustworthy federated machine learning, requirements from the perspective of different principles and different federated machine learning participants, and methods to realize trustworthy federated machine learning. Also provided is guidance on how trustworthy federated machine learning is used in various scenarios.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3187\u002F10959\u002F","2026-01-27T15:52:32.325Z","2026-01-27T15:54:37.606Z","2026-01-27T15:54:37.677Z",{"id":4503,"documentId":4504,"number":4505,"title":4506,"part":11,"abstract":4507,"lastUpdatedYear":524,"linkToOfficial":4508,"createdAt":4509,"updatedAt":4510,"publishedAt":4511,"featured":11},243,"jl2gnpggweqhip71fwn866oh","IEEE 3152-2024","IEEE Standard for Transparent Human and Machine Agency Identification","\u003Cp>Elements to improve transparency in the identification of the agency behind media or communications, such as machine intelligence, a human being, or a combination of entities, are defined in this standard. The goal is to facilitate transparency, understanding, and trust during online, telephone, robotic, or other electronic interactions, thereby strengthening provisions for public safety and security.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3152\u002F11718\u002F","2026-01-27T16:41:25.273Z","2026-01-27T16:43:02.122Z","2026-01-27T16:43:02.204Z",{"id":4513,"documentId":4514,"number":4515,"title":4516,"part":11,"abstract":4517,"lastUpdatedYear":524,"linkToOfficial":4518,"createdAt":4519,"updatedAt":4520,"publishedAt":4521,"featured":11},245,"vzjaxh1b3a8srgyiliixaohw","IEEE 3198-2025","IEEE Standard for Evaluation Method of Machine Learning Fairness","\u003Cp>A method for evaluating the fairness of machine learning is specified in this standard. Multiple causes contribute to the unfairness of machine learning. These causes of machine learning unfairness are categorized. The widely recognized and used definitions of machine learning fairness are presented. Various metrics corresponding to the definitions, and how to calculate the metrics are specified in this standard. Detailed conditions and procedures to set up the tests for evaluating machine learning fairness are given by the test cases in this document.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3198\u002F11068\u002F","2026-01-27T16:43:06.658Z","2026-01-27T16:44:28.993Z","2026-01-27T16:44:29.037Z",{"id":4523,"documentId":4524,"number":4525,"title":4526,"part":11,"abstract":4527,"lastUpdatedYear":524,"linkToOfficial":4528,"createdAt":4529,"updatedAt":4530,"publishedAt":4531,"featured":11},247,"tfwpcz7qbftm4o9ihgedp9kc","IEEE 3350-2025","IEEE Recommended Practice for Improving Generalizability of Artificial Intelligence for Medical Imaging","\u003Cp>The contents of this Recommended Practice delineates an architecture and offers suggestions for enhancing the generalizability of artificial intelligence (AI) models in medical imaging.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3350\u002F11222\u002F","2026-01-27T16:44:34.020Z","2026-01-27T16:46:05.537Z","2026-01-27T16:46:05.586Z",{"id":1592,"documentId":4533,"number":4534,"title":4535,"part":11,"abstract":4536,"lastUpdatedYear":1415,"linkToOfficial":4537,"createdAt":4538,"updatedAt":4539,"publishedAt":4540,"featured":11},"v8d2sx1kq3ankrvu4a6mfqns","IEEE 3168-2024","IEEE Standard for Robustness Evaluation Test Methods for a Natural Language Processing Service That Uses Machine Learning","\u003Cp>The natural language processing (NLP) services using machine learning have rich applications in solving various tasks and have been widely deployed and used, usually accessible by application programming interface (API) calls. The robustness of the NLP services is challenged by various well-known general corruptions and adversarial attacks. Inadvertent or random deletion, addition, or repetition of characters or words are examples of general corruptions. Adversarial characters, words, or sentence samples are generated by adversarial attacks, causing the models underpinning the NLP services to produce incorrect results. A method for quantitatively evaluating the robustness the NLP services is proposed by this standard. Under the method, different cases the evaluation needs to perform against are specified. Robustness metrics and their calculation are defined. With the standard, understanding of the robustness of the services can be developed by the service stakeholders including the service developer, service providers, and service users. The evaluation can be performed during various phases in the life cycle of the NLP services, the testing phase, in the validation phase, after deployment, and so forth.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3168\u002F10923\u002F","2026-01-27T16:46:10.112Z","2026-03-06T06:43:52.037Z","2026-03-06T06:43:52.195Z",{"id":4542,"documentId":4543,"number":4544,"title":4545,"part":11,"abstract":4546,"lastUpdatedYear":276,"linkToOfficial":4547,"createdAt":4548,"updatedAt":4549,"publishedAt":4550,"featured":11},251,"jzq5bgk6c3ziw0u0bbsladvz","IEEE 3300-2022","IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Multimodal Conversion Version 1.2","\u003Cp>This standard adopts MPAI Technical Specification Version 1.2 as an IEEE Standard. Multimodal Conversation (MPAI-MMC) is an MPAI Standard comprising five use cases, all sharing the use of artificial intelligence (AI) to enable a form of human-machine conversation in completeness and intensity.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3300\u002F11005\u002F","2026-01-27T16:48:02.949Z","2026-01-27T16:49:24.809Z","2026-01-27T16:49:24.853Z",{"id":1090,"documentId":4552,"number":4553,"title":4554,"part":11,"abstract":4555,"lastUpdatedYear":276,"linkToOfficial":4556,"createdAt":4557,"updatedAt":4558,"publishedAt":4559,"featured":11},"bznch7d3xn8svyzz3xj3m567","IEEE 3303-2023","IEEE Standard Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Compression and Understanding of Industrial Data 1.1","\u003Cp>This standard adopts MPAI Technical Specification Version 1.1 as an IEEE Standard. The Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Compression and Understanding of Industrial Data (MPI-CUI) Version 1.1predicts the performance of a Company from its Governance, Financial and Risk data in a Prediction Horizon expression as Default Probability, adequacy Index of Organizational Model, and Business Continuity Index.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3303\u002F11134\u002F","2026-01-27T16:49:30.147Z","2026-01-27T16:51:02.513Z","2026-01-27T16:51:02.579Z",{"id":1464,"documentId":4561,"number":4562,"title":4563,"part":11,"abstract":4564,"lastUpdatedYear":1415,"linkToOfficial":4565,"createdAt":4566,"updatedAt":4567,"publishedAt":4568,"featured":11},"u4raddxzxnmpts7kils1ezny","IEEE 2986-2023","IEEE Recommended Practice for Privacy and Security for Federated Machine Learning","\u003Cp>Privacy and security issues pose great challenges to the federated machine leaning (FML) community. A general view on privacy and security risks while meeting applicable privacy and security requirements in FML is provided. This recommended practice is provided in four parts: malicious failure and non-malicious failure in FML, privacy and security requirements from the perspective of system and FML participants, defensive methods and fault recovery methods, and the privacy and security risks evaluation. It also provides some guidance for typical FML scenarios in different industry areas, which can facilitate practitioners to use FML in a better way.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F2986\u002F10564\u002F","2026-01-27T16:51:10.598Z","2026-03-06T06:41:41.650Z","2026-03-06T06:41:41.702Z",{"id":1115,"documentId":4570,"number":4571,"title":4572,"part":11,"abstract":4573,"lastUpdatedYear":524,"linkToOfficial":4574,"createdAt":4575,"updatedAt":4576,"publishedAt":4577,"featured":11},"awt6r2x0ciljia1upudeeddl","IEEE 3410-2025","IEEE Guide for Large-Scale Financial Risk Management Models","\u003Cp>This guide aims to establish a standardized reference framework and technical protocols for implementing large-scale artificial intelligence (AI) models in financial risk management. It serves as an essential guidance for financial institutions seeking to build, iterate, utilize their large-scale AI models for managing financial risks. The guide offers best practice on integrating various data knowledge, including feature space, sample, and model knowledge, into large-scale financial risk management models. It also provides strategic guidance on designing pre-training process, fine-tuning process, and evaluation methodologies to augment the feature and risk comprehensive capabilities of the large-scale models. Furthermore, it elucidates on the swift adaptation of the large models to various financial lending risk scenarios and the iterative process of the large-scale models.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3410\u002F11382\u002F","2026-01-27T16:52:55.645Z","2026-01-27T16:54:31.116Z","2026-01-27T16:54:31.157Z",{"id":4579,"documentId":4580,"number":4581,"title":4582,"part":11,"abstract":4583,"lastUpdatedYear":1415,"linkToOfficial":4584,"createdAt":4585,"updatedAt":4586,"publishedAt":4587,"featured":11},259,"vz7xfirfnm95cw2ezfw1eg57","IEEE 3304-2024","IEEE Standard for Adoption of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) Technical Specification Neural Network Watermarking (NNW) V1","\u003Cp>This is an adoption of the Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI)--Technical Specification Neural Network Watermarking as an IEEE Standard. The Neural Network Watermarking (MPAI-NNW) Technical Specification provides standard methods to measure the ability of 1) a watermark inserter to inject a payload without deteriorating the neural network (NN) performance, 2) a watermark detector to recognize the presence and the watermark decoder to successfully retrieve the payload of the inserted watermark, and 3) a watermark inserter to inject a payload and the watermark detector\u002Fdecoder to detect\u002Fdecode a payload from a watermarked model or any of its inferences at a measured computational cost.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3304\u002F11281\u002F","2026-01-27T16:54:42.130Z","2026-01-27T16:56:17.808Z","2026-01-27T16:56:17.886Z",{"id":4589,"documentId":4590,"number":4591,"title":4592,"part":11,"abstract":4593,"lastUpdatedYear":276,"linkToOfficial":4594,"createdAt":4595,"updatedAt":4596,"publishedAt":4597,"featured":11},261,"gri1zul31y0t1gu3zkywf8ys","IEEE 3161-2022","IEEE Standard for Digital Retina Systems","\u003Cp>The aspects of reference architecture, technical characteristics, components, and functional requirements of digital retina systems for real-time analysis and processing of video big data are discussed in this standard.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3161\u002F10879\u002F","2026-01-27T16:56:29.172Z","2026-01-27T16:58:04.407Z","2026-01-27T16:58:04.451Z",{"id":1127,"documentId":4599,"number":4600,"title":4601,"part":11,"abstract":4602,"lastUpdatedYear":276,"linkToOfficial":4603,"createdAt":4604,"updatedAt":4605,"publishedAt":4606,"featured":11},"ndncewzrx1pmc46qw8lxhe9j","IEEE 2941.1-2022","IEEE Standard for Operator Interfaces of Artificial Intelligence","\u003Cp>A set of operator interfaces frequently used in artificial intelligence (AI) applications is defined in this standard, where the AI operators refer to the standard building blocks and primitives for performing basic AI operations. The functionality and the specific input and output operands of an AI operator are discussed, as well as both generality and efficiency. Various types of operators, such as those related to basic mathematics, neural network, and machine learning, are highlighted.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F2941.1\u002F10567\u002F","2026-01-27T16:58:14.452Z","2026-01-27T17:00:28.548Z","2026-01-27T17:00:28.629Z",{"id":1458,"documentId":4608,"number":4609,"title":4610,"part":11,"abstract":4611,"lastUpdatedYear":276,"linkToOfficial":4612,"createdAt":4613,"updatedAt":4614,"publishedAt":4615,"featured":11},"li41qsovxyshdknhk96892kn","IEEE 2941.2-2023","IEEE Standard for Application Programming Interfaces (APIs) for Deep Learning (DL) Inference Engines","\u003Cp>A set of application programming interfaces (APIs) that is aimed at breaking down the barriers between different deep learning (DL) inference engines and applications is defined in this standard. The APIs are comprised of functional interfaces including parameter reading, model compilation optimization, operator registration, thread management, input\u002Foutput data acquisition, inference instance creation, and inference execution.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F2941.2\u002F10963\u002F","2026-01-27T17:00:33.841Z","2026-03-06T06:41:11.746Z","2026-03-06T06:41:11.849Z",{"id":1151,"documentId":4617,"number":4618,"title":4619,"part":11,"abstract":4620,"lastUpdatedYear":524,"linkToOfficial":4621,"createdAt":4622,"updatedAt":4623,"publishedAt":4624,"featured":11},"lmgro9d9e9b3keisr76hw2bn","IEEE 3119-2025","IEEE Standard for the Procurement of Artificial Intelligence and Automated Decision Systems","\u003Cp>The standard helps procurement teams reduce risks in artificial intelligence systems (AIS) by using tailored risk management practices when purchasing AIS. Specific process steps for AIS problem definition, solicitation preparation, vendor and solution evaluation, contract negotiation, and contract monitoring are described. Risk management methodologies that augment customary activities and tasks performed during the procurement life cycle are provided including identifying, analyzing, evaluating, prioritizing, mitigating, and controlling unique AIS risks that can detract from unique AIS benefits. How procurement teams can use and apply practical AIS tools and metrics is also provided. The standard focuses explicitly on AIS risks (when compared to traditional, non-AIS technology) and is designed to address the purchase of commercial AI products and services procured using a formal contract or contract framework.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3119\u002F10729\u002F","2026-01-27T17:04:42.527Z","2026-01-27T17:06:25.392Z","2026-01-27T17:06:25.432Z",{"id":1445,"documentId":4626,"number":4627,"title":4628,"part":11,"abstract":4629,"lastUpdatedYear":276,"linkToOfficial":4630,"createdAt":4631,"updatedAt":4632,"publishedAt":4633,"featured":11},"iy229ojfvkhubopm843ko5b7","IEEE 2802-2022","IEEE Standard for Performance and Safety Evaluation of Artificial Intelligence Based Medical Devices: Terminology","\u003Cp>This standard is aimed at establishing concepts and terminology for the performance and safety evaluation of artificial intelligence medical device, which covers basic technology, dataset, quality characteristics, quality evaluation and application scenario. The annex further provides basic equations for quality evaluation purpose.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F2802\u002F7460\u002F","2026-01-27T17:07:07.526Z","2026-03-06T06:40:31.155Z","2026-03-06T06:40:31.220Z",{"id":1586,"documentId":4635,"number":4636,"title":4637,"part":11,"abstract":4638,"lastUpdatedYear":276,"linkToOfficial":4639,"createdAt":4640,"updatedAt":4641,"publishedAt":4642,"featured":11},"l5exnvrodswjgxtlblvvw7t9","IEEE 3129-2023","IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service","\u003Cp>Test specifications with a set of indicators for common corruption and adversarial attacks, which can be used to evaluate the robustness of artificial intelligence-based image recognition services are provided in this standard. Robustness attack threats and establishes an assessment framework to evaluate the robustness of artificial intelligence-based image recognition service under various settings are also specified in this standard.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3129\u002F10747\u002F","2026-01-27T17:09:01.087Z","2026-03-06T06:41:58.608Z","2026-03-06T06:41:58.675Z",{"id":1452,"documentId":4644,"number":4645,"title":4646,"part":11,"abstract":4647,"lastUpdatedYear":524,"linkToOfficial":4648,"createdAt":4649,"updatedAt":4650,"publishedAt":4651,"featured":11},"czd7y0lhtcjoauohvmztq6tt","IEEE 2840-2024","IEEE Standard for Responsible AI Licensing","\u003Cp>The contents within IEEE Std 2840™ pertains to licenses which, by conforming to this standard, meaningfully engages with the spirit of responsible use of AI systems. In this regard, the standard is not intended to define what a license or contract is as these are well understood by legal systems across the world. Rather, licenses conforming to the IEEE Std 2840 standard shall adhere to and specify clauses that pertain to dimensions of use (i.e., capabilities, consequences, and areas of application) of specific technical artifacts.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F2840\u002F7673\u002F","2026-01-27T17:10:33.955Z","2026-03-06T06:40:51.013Z","2026-03-06T06:40:51.131Z",{"id":4653,"documentId":4654,"number":4655,"title":4656,"part":11,"abstract":4657,"lastUpdatedYear":276,"linkToOfficial":4658,"createdAt":4659,"updatedAt":4660,"publishedAt":4661,"featured":11},332,"xpozcuyn9jxq4kslwuo9vp8v","IEEE 2671-2022","IEEE Standard for General Requirements of Online Detection Based on Machine Vision in Intelligent Manufacturing","\u003Cp>General requirements of online detection based on machine vision, including requirements for data format, data transmission processes, definition of application scenarios and performance metrics for evaluating the effect of online detection deployment are specified in this standard.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F2671\u002F7176\u002F","2026-01-27T17:18:04.778Z","2026-03-06T06:40:04.771Z","2026-03-06T06:40:04.908Z",{"id":1223,"documentId":4663,"number":4664,"title":4665,"part":11,"abstract":4666,"lastUpdatedYear":524,"linkToOfficial":4667,"createdAt":4668,"updatedAt":4669,"publishedAt":4670,"featured":11},"m2rqaxt2ymit3caqpraahand","IEEE 7009-2024","IEEE Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems","\u003Cp>A practical, technical baseline of specific methodologies and tools for the development, implementation, and use of effective fail-safe mechanisms in autonomous and semi-autonomous systems is established in this standard. The standard serves as the basis for developers, as well as users and regulators, to design fail-safe mechanisms in a robust, transparent, and accountable manner.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7009\u002F7096\u002F","2026-01-27T17:19:54.107Z","2026-01-27T17:21:45.741Z","2026-01-27T17:21:45.794Z",{"id":4672,"documentId":4673,"number":4674,"title":4675,"part":11,"abstract":4676,"lastUpdatedYear":1415,"linkToOfficial":4677,"createdAt":4678,"updatedAt":4679,"publishedAt":4680,"featured":11},285,"w3zi2m1a32huiak8g6vgcoip","IEEE 7014-2024","IEEE Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems","\u003Cp>Guidance and actions for the ethical development, deployment, or decommission of autonomous and intelligent systems that attempt to emulate aspects of human empathy are provided by this standard.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F7014\u002F7648\u002F","2026-01-27T17:24:14.951Z","2026-01-27T17:25:45.951Z","2026-01-27T17:25:46.007Z",{"id":4682,"documentId":4683,"number":4684,"title":4685,"part":11,"abstract":4686,"lastUpdatedYear":171,"linkToOfficial":4687,"createdAt":4688,"updatedAt":4689,"publishedAt":4690,"featured":11},294,"vhqlyf2xlszdvl6yd5ebjo3g","ITU-T X Suppl. 37 (09\u002F2022) - ITU-T X.1231","Supplement on countering spam based on machine learning","\u003Cp>This Supplement 37 to Recommendation ITU-T X.1231 defines a technical framework for countering spam based on machine learning (ML). It may help some relevant persons and companies in spam management, reduce the benefit losses of users and providers, improve user experience and promote the healthy development of telecommunication business. This Supplement to Recommendation ITU-T X.1231 provides some general scenarios, and characteristics of spam, and defines the general technical framework, and work flows about countering spam based on ML.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15119&lang=en","2026-01-27T17:31:31.322Z","2026-01-27T17:33:04.442Z","2026-01-27T17:33:04.527Z",{"id":4692,"documentId":4693,"number":4694,"title":4695,"part":11,"abstract":4696,"lastUpdatedYear":171,"linkToOfficial":4697,"createdAt":4698,"updatedAt":4699,"publishedAt":4700,"featured":11},296,"pxd6kl9gnt77gy4v8p7yx4u7","ITU-T Y Suppl. 72 (11\u002F2022) - ITU-T Y.3000-series","Artificial intelligence standardization roadmap","\u003Cp>Supplement 72 to ITU-T Y-series Recommendations provides the standardization roadmap for artificial intelligence (AI) in the field of information and communication technology (ICT). This AI standardization roadmap has been developed to assist in the development of AI related standards in the ICT field by providing information on existing standards and standards under development in key standards development organizations (SDOs). In addition, it provides overviews of AI and AI related technical areas from the standards perspective, AI related activities in SDOs, and gap analysis.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=152","2026-01-27T17:33:19.890Z","2026-01-27T17:35:12.893Z","2026-01-27T17:35:12.984Z",{"id":4702,"documentId":4703,"number":4704,"title":4705,"part":11,"abstract":4706,"lastUpdatedYear":276,"linkToOfficial":4707,"createdAt":4708,"updatedAt":4709,"publishedAt":4710,"featured":11},298,"renlsm9o96sljkqzkjgebhpo","ITU-T M.3384 (04\u002F2023)","Intelligence levels of artificial intelligence enhanced telecom operation and management","\u003Cp>This provides definitions, classifications, object selection and an automatic evaluating mechanism for the evaluation of the intelligence levels of artificial intelligence-enhanced telecom operation and management systems that comply with the framework specified in Recommendation ITU-T M.3080.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15517&lang=en","2026-01-27T17:35:35.113Z","2026-01-27T17:37:13.255Z","2026-01-27T17:37:13.311Z",{"id":4712,"documentId":4713,"number":4714,"title":4715,"part":11,"abstract":4716,"lastUpdatedYear":276,"linkToOfficial":4717,"createdAt":4718,"updatedAt":4719,"publishedAt":4720,"featured":11},300,"tb4qgrfy0thif6bnkldyk33j","ITU-T M.3385 (04\u002F2023)","Intelligence levels evaluation framework of artificial intelligence enhanced telecom operation and management","\u003Cp>This provides a detailed evaluation framework, evaluation rating method and automatic evaluating process for intelligence levels of systems which follow the framework of artificial intelligence enhanced telecom operation and management (AITOM). This Recommendation is included in the series of Recommendations about intelligence levels of artificial intelligence enhanced telecom operation and management (IL-AITOM).\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15518&lang=en","2026-01-27T17:37:25.514Z","2026-01-27T17:39:32.537Z","2026-01-27T17:39:32.572Z",{"id":1499,"documentId":3808,"number":3809,"title":3810,"part":11,"abstract":3811,"lastUpdatedYear":524,"linkToOfficial":3812,"createdAt":3813,"updatedAt":3814,"publishedAt":3815,"featured":11},{"id":4723,"documentId":4724,"number":4725,"title":4726,"part":11,"abstract":4727,"lastUpdatedYear":171,"linkToOfficial":4728,"createdAt":4729,"updatedAt":4730,"publishedAt":4731,"featured":11},304,"omo02f660ilzji7fg1wip9hr","ITU-T L Suppl. 53 (10\u002F2022)","Guidelines on the implementation of environmental efficiency criteria for artificial intelligence and other emerging technologies","\u003Cp>Supplement 53 to ITU-T L-series Recommendations provides guidelines to policy-makers, technologists, innovators, environmentalists and other stakeholders from the technology industry, environmental sciences and policy arena on the topic of environmental efficiency criteria to assess the environmental impacts of artificial intelligence and other emerging technologies. These guidelines aim to serve as common factors for the above-mentioned stakeholders to consider while developing, deploying and promoting any piece of technology into the market and society, rather providing than a comprehensive list of criteria. While \"emerging technologies\" is a broad term, this Supplement identifies a few sample technologies through their accordant applications and areas of work in 16 applicable industry domains, which stakeholders can use as references to improve the environmental efficiency of their own technological products and\u002For services. When discussing environmental efficiency, this Supplement approaches environmental efficiency criteria from an adjusted model of life-cycle assessment of a product, within which three stages of environmental impacts – materials, use and end of life – are examined. The Supplement provides both long-term and short-term strategies, which include not only specific examples for certain technologies addressing the three stages of environmental efficiency, but also an instrument to be used to localize such guidelines as well as to allow global benchmarking.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15169&lang=en","2026-01-27T17:41:43.688Z","2026-01-27T17:43:31.697Z","2026-01-27T17:43:31.761Z",{"id":1340,"documentId":4733,"number":4734,"title":4735,"part":11,"abstract":4736,"lastUpdatedYear":276,"linkToOfficial":4737,"createdAt":4738,"updatedAt":4739,"publishedAt":4740,"featured":11},"uaaczopyg94nar8islw4zz14","ITU-T H.862.6 (09\u002F2023)","Functional requirements for counselling services based on artificial emotional intelligence","\u003Cp>Recommendation ITU-T H.862.6 proposes service requirements and functional specifications for counselling services based on artificial emotional intelligence (AEI) technologies. This Recommendation proposes a service model for counselling services using several scenarios. At a time when artificial intelligence (AI) technologies are being widely proposed and used, the relevant standards can be an important opportunity to facilitate the development of the technology in the industry.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15635&lang=en","2026-01-27T17:43:36.598Z","2026-01-27T17:45:10.793Z","2026-01-27T17:45:10.859Z",{"id":1353,"documentId":4742,"number":4743,"title":4744,"part":11,"abstract":4745,"lastUpdatedYear":276,"linkToOfficial":4746,"createdAt":4747,"updatedAt":4748,"publishedAt":4749,"featured":11},"o8fq36b2gud1xo9lxzon9a7v","ITU-T Y.3183 (01\u002F2023)","Framework for network slicing management assisted by machine learning leveraging quality of experience feedback from verticals","\u003Cp>Recommendation ITU-T Y.3183 provides a framework for machine learning-assisted network slicing management, leveraging vertical end users' feedback on quality of experience (QoE), which can help achieve the runtime optimization of user perceived performance. The overall architecture, components, workflow and related APIs of this framework are specified with respect to the high-level requirements identified. A use case is provided in Appendix I to show an application example of this framework. Example implementations of the key application programming interfaces (APIs) are also provided.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15238&lang=en","2026-01-27T17:45:15.504Z","2026-01-27T17:47:01.222Z","2026-01-27T17:47:01.311Z",{"id":1365,"documentId":4751,"number":4752,"title":4753,"part":11,"abstract":4754,"lastUpdatedYear":171,"linkToOfficial":4755,"createdAt":4756,"updatedAt":4757,"publishedAt":4758,"featured":11},"hdyb9iq3ott9qao64sipa05c","ITU-T L Suppl. 48 (07\u002F2022)","Data centre energy saving: Application of artificial intelligence technology in improving energy efficiency of telecommunication room and data centre infrastructure","\u003Cp>Telecommunication room and data centre (DC) infrastructure includes a large number of items of information and communication equipment. In order to keep equipment running continuously and reliably, the rooms where it is located will necessarily be equipped with air conditioners to create a suitable environment for equipment operation. This will cause a large amount of energy consumption and carbon emissions. This Supplement focuses on the application of AI technology and other emerging technologies such as digital twins to improve the energy efficiency and reduce the carbon emissions of telecommunication room and DC infrastructures. Most of the existing telecommunication room and DC infrastructures do not have the full ability to identify the distribution of indoor temperatures. Therefore, it is difficult to analyse the heat flow and its related power consumption in real time and make appropriate adjustments in a timely manner. Consequently, this leads to unnecessary consumption of energy. Supplement 48 to the ITU-T L-series Recommendations will address how AI-based power management can provide the following capabilities: Data collections in telecommunication room and DC infrastructure; Real-time analysis of the historical power consumption and parameters of the target equipment room; The ability to train an intelligent model; Making reasonable adjustments dynamically to the air conditioning and temperature, so as to achieve energy saving in the telecommunication room and DC infrastructure.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15085&lang=en","2026-01-27T17:47:06.394Z","2026-01-27T17:48:58.395Z","2026-01-27T17:48:58.448Z",{"id":4760,"documentId":4761,"number":4762,"title":4763,"part":11,"abstract":4764,"lastUpdatedYear":276,"linkToOfficial":4765,"createdAt":4766,"updatedAt":4767,"publishedAt":4768,"featured":11},312,"roy8omzs7n09pkycj4i4nduo","ITU-T Y.3656 (06\u002F2023)","Big data driven networking-mechanism of network service provisioning","\u003Cp>Compared with the traditional network, Recommendation ITU-T Y.3656 can provide better integration and more intelligent capabilities, such as the capabilities of self-optimization, self-configuration, and intelligent fault management, based on big data plane and its machine learning capabilities. This Recommendation can provide a significant enhancement to network service provisioning by using big data intelligence. This Recommendation specifies the network service provisioning mechanism in big data driven networking (bDDN).\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15539&lang=en","2026-01-27T17:49:04.856Z","2026-01-27T17:50:46.406Z","2026-01-27T17:50:46.454Z",{"id":1319,"documentId":4770,"number":4771,"title":4772,"part":11,"abstract":4773,"lastUpdatedYear":171,"linkToOfficial":4774,"createdAt":4775,"updatedAt":4776,"publishedAt":4777,"featured":11},"l1tplqpcy2lvbt28aoeubqm2","ITU-T Y.3181 (09\u002F2022)","Architectural framework for machine learning sandbox in future networks including IMT-2020","\u003Cp>Recommendation ITU-T Y.3181 provides an architectural framework for machine learning (ML) sandbox in future networks including IMT-2020. More precisely, it describes requirements and high level architecture for ML sandbox in future networks including IMT-2020.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Fmyworkspace\u002F#\u002Ft-rec\u002Fitem?id=15058","2026-01-27T17:51:10.531Z","2026-01-27T17:52:46.778Z","2026-01-27T17:52:46.827Z",{"id":4779,"documentId":4780,"number":4781,"title":4782,"part":11,"abstract":4783,"lastUpdatedYear":60,"linkToOfficial":4784,"createdAt":4785,"updatedAt":4786,"publishedAt":4787,"featured":11},316,"oskpalfmuxnmt91apl3xouhl","ITU-T F.749.4 (06\u002F2021)","Use cases and requirements for multimedia communication enabled vehicle systems using artificial intelligence","\u003Cp>Recommendation ITU-T F.749.4 describes use cases and scenarios, high-layer architecture, service and network requirements, functional requirements and non-functional requirements for multimedia communication enabled vehicle systems using artificial intelligence.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002FITU-T\u002Frecommendations\u002Frec.aspx?rec=14683&lang=en","2026-01-27T17:52:52.834Z","2026-01-27T17:54:43.110Z","2026-01-27T17:54:43.157Z",{"id":1371,"documentId":4789,"number":4790,"title":4791,"part":11,"abstract":4792,"lastUpdatedYear":958,"linkToOfficial":4793,"createdAt":4794,"updatedAt":4795,"publishedAt":4796,"featured":11},"ekj74cjwxyh1nvzwsscjip0m","ITU-T Y Suppl. 63 (07\u002F2020)","Unlocking Internet of things with artificial intelligence","\u003Cp>Supplement 63 to ITU-T Y-series Recommendations examines how artificial intelligence could step in to bolster the intent of urban stakeholders to deploy Internet of things (IoT) technologies and eventually transition to smart cities. The main elements examined in this Supplement are: The various technological implementations of artificial intelligence (AI) that may facilitate smart cities technologies; The role played by AI in managing the data generated within the IoT realm and urban spaces; The main benefits of adopting AI and delving into how this technology could be leveraged to attain the Sustainable Development Goals (SDGs).\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002FITU-T\u002Frecommendations\u002Frec.aspx?rec=14370&lang=en","2026-01-27T17:54:47.686Z","2026-01-27T17:56:18.326Z","2026-01-27T17:56:18.376Z",{"id":1427,"documentId":4798,"number":4799,"title":4800,"part":11,"abstract":4801,"lastUpdatedYear":171,"linkToOfficial":4802,"createdAt":4803,"updatedAt":4804,"publishedAt":4805,"featured":11},"yz946dywc2cbhi2ahyrqqt4u","ITU-T Y.3116 (02\u002F2022)","Traffic typization IMT-2020 management based on an artificial intelligence approach","\u003Cp>At present, the standardization of IMT-2020 networks aims at dealing with architectural issues (infrastructure and new services), analysing and ensuring signalling at the management level and ensuring the quality and security of IoT services. As is well known, according to ITU-R Recommendation ITU-R M.2083-0 \"IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond\", one of the IMT-2020 infrastructure technologies is software-defined networking (SDN). Given the heterogeneous nature of the traffic, it is necessary to ensure efficient and effective infrastructure management. With a view to increasing the effectiveness of the automation of management, the use of artificial intelligence (AI) technologies needs to be considered for traffic detection and typization. In this way, Recommendation ITU-T Y.3116 considers an overview of machine learning (ML) technologies for traffic detection and a method for the traffic typization and recognition for IMT-2020 management based on an ML approach.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002Frec\u002FT-REC-Y.3116-202202-I","2026-01-27T17:56:22.961Z","2026-01-27T17:57:52.952Z","2026-01-27T17:57:52.998Z",{"id":1439,"documentId":4807,"number":4808,"title":4809,"part":11,"abstract":4810,"lastUpdatedYear":60,"linkToOfficial":4811,"createdAt":4812,"updatedAt":4813,"publishedAt":4814,"featured":11},"ebh4bla4dyi9sbu9h7s6nly2","ITU-T F.748.13 (06\u002F2021)","Technical framework for the shared machine learning system","\u003Cp>Recommendation ITU-T F.748.13 defines the roles, technical and security requirements of a shared machine learning system, and provides technical architectures, functional components, and processing procedures of the shared machine learning system in the centralized and decentralized modes.\u003C\u002Fp>","https:\u002F\u002Fwww.itu.int\u002FITU-T\u002Frecommendations\u002Frec.aspx?rec=14682&lang=en","2026-01-27T17:58:15.600Z","2026-01-27T18:00:01.135Z","2026-01-27T18:00:01.318Z",{"id":73,"documentId":74,"name":75,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":4816,"hash":78,"ext":79,"mime":80,"size":81,"url":82,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":83,"updatedAt":84,"publishedAt":85},{},{"id":3402,"documentId":3403,"title":3404,"createdAt":3405,"updatedAt":3406,"publishedAt":3407,"ai_standards":4818,"icon":4840},[4819,4820,4830],{"id":3392,"documentId":3393,"number":3394,"title":3346,"part":3395,"abstract":3396,"lastUpdatedYear":524,"linkToOfficial":3397,"createdAt":3398,"updatedAt":3399,"publishedAt":3400,"featured":11},{"id":4821,"documentId":4822,"number":4823,"title":4824,"part":11,"abstract":4825,"lastUpdatedYear":524,"linkToOfficial":4826,"createdAt":4827,"updatedAt":4828,"publishedAt":4829,"featured":11},229,"ev7lydo2rb0bflh3oai4mm7m","IEEE 3378-2025","IEEE Approved Draft Standard for Framework and Process for Large-Scale Deep Learning Model Evaluation","\u003Cp>The tasks that large-scale deep learning model can support were specified in this standard. The evaluation metrics and evaluation requirements of large-scale deep learning models were addressed in this standard.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3378\u002F11304\u002F","2026-01-27T16:29:08.839Z","2026-01-27T16:30:54.247Z","2026-01-27T16:30:54.309Z",{"id":4831,"documentId":4832,"number":4833,"title":4834,"part":11,"abstract":4835,"lastUpdatedYear":276,"linkToOfficial":4836,"createdAt":4837,"updatedAt":4838,"publishedAt":4839,"featured":11},231,"dtkn9wk4ny3gugqz9zpxrrjk","IEEE P3374","Recommended Practice for the Framework and Evaluation Methods of Audio-Driven Portraits Based on Artificial Intelligence","\u003Cp>This recommended practice specifies framework and evaluation methods of audio-driven portraits based on artificial intelligence. In this recommended practice, the framework includes functional modules and input\u002Foutput interfaces that implement facial and body movements that drive portraits based on audio. The input interface includes the size and resolution of portraits. The output interface provides a mechanism for exporting generated videos. The framework includes modules for generating facial expressions, lip shapes, body movements, and super-resolution. This recommended practice also specifies evaluation methods based on audio-driven portrait models.\u003C\u002Fp>","https:\u002F\u002Fstandards.ieee.org\u002Fieee\u002F3374\u002F11301\u002F","2026-01-27T16:30:59.461Z","2026-01-27T16:32:53.225Z","2026-01-27T16:32:53.265Z",{"id":2983,"documentId":3409,"name":3410,"alternativeText":11,"caption":11,"width":76,"height":76,"formats":4841,"hash":3412,"ext":79,"mime":80,"size":3413,"url":3414,"previewUrl":11,"provider":39,"provider_metadata":11,"createdAt":3415,"updatedAt":3416,"publishedAt":3415},{},1773979098307]