| 1 |
General-use Responsible AI and Risk Management Frameworks |
| 1.1 |
Government, Regulatory & Standards |
| 1.1.1 |
AI Risk Management Framework (AI RMF) |
National Institute of Standards and Technology (NIST) |
https://www.nist.gov/itl/ai-risk-management-framework |
| 1.1.2 |
NIST AI RMF Playbook |
National Institute of Standards and Technology (NIST) |
https://airc.nist.gov/airmf-resources/playbook |
| 1.1.3 |
ISO/IEC 23894:2023: Information technology – Artificial intelligence – Guidance on risk management |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/77304.html |
| 1.1.4 |
Model Artificial Intelligence Governance Framework (2nd Edition) |
Infocomm Media Development Authority and Personal Data Protection Commission (PDPC) Singapore |
https://www.pdpc.gov.sg/-/media/Files/PDPC/PDF-Files/Resource-for-Organisation/AI/SGModelAIGovFramework2.pdf |
| 1.1.5 |
Guidance on the Ethical Development and Use of Artificial Intelligence |
Office of the Privacy Commissioner of Personal Data, Hong Kong |
https://www.pcpd.org.hk/english/resources_centre/publications/files/guidance_ethical_e.pdf |
| 1.1.6 |
Australia’s AI Ethics Principles |
Australian Government – Department of Industry, Science and Resources |
https://www.industry.gov.au/publications/australias-artificial-intelligence-ethics-principles/australias-ai-ethics-principles?utm_source=StartupNewsAsia |
| 1.1.7 |
Data Protection Audit Framework | Artificial Intelligence Toolkit |
UK Information Commissioner’s Office |
https://ico.org.uk/for-organisations/advice-and-services/audits/data-protection-audit-framework/toolkits/artificial-intelligence/ |
| 1.1.8 |
Artificial Intelligence |
Federal Office for Information Security |
https://www.bsi.bund.de/EN/Themen/Unternehmen-und-Organisationen/Informationen-und-Empfehlungen/Kuenstliche-Intelligenz/kuenstliche-intelligenz_node.html |
| 1.2 |
Corporate |
| 1.2.1 |
Microsoft Responsible AI Standard, v2 – General Requirements |
Microsoft (~221,000 employees) |
https://msblogs.thesourcemediaassets.com/sites/5/2022/06/Microsoft-Responsible-AI-Standard-v2-General-Requirements-3.pdf |
| 1.2.10 |
AI Safety and Security |
Frontier Model Forum |
https://www.frontiermodelforum.org/about-us |
| 1.2.2 |
Our AI Principles |
Google (Alphabet, ~190,234 employees) |
https://ai.google/principles |
| 1.2.3 |
AI Ethics Maturity Model |
Salesforce (70,000+ employees) |
https://www.salesforceairesearch.com/static/ethics/EthicalAIMaturityModel.pdf |
| 1.2.4 |
The Aletheia Framework 2.0 |
Rolls-Royce (50,000+ employees) |
https://www.rolls-royce.com/~/media/Files/R/Rolls-Royce/documents/stand-alone-pages/aletheia-framework-booklet-2021.pdf |
| 1.2.5 |
From Principles to Practice – An interdisciplinary framework to operationalise AI ethics |
AI Ethics Impact Group led by VDE and BertelsmannStiftung (85,000 employees) |
https://www.ai-ethics-impact.org/resource/blob/1961130/c6db9894ee73aefa489d6249f5ee2b9f/aieig---report---download-hb-data.pdf |
| 1.2.6 |
AI Ethics Framework |
Digital Catapult |
https://www.digicatapult.org.uk/wp-content/uploads/2023/06/DC_AI_Ethics_Framework-2021.pdf |
| 1.2.7a |
AI Ethics Principles |
Samsung Electronics (260,000+ employees) |
https://www.samsung.com/global/sustainability/policy-file/AZEqcluaBekALYM9/Samsung_Electronics_AI_Ethics_EN.pdf |
| 1.2.7b |
AI Safety Framework |
Samsung Electronics (260,000+ employees) |
https://www.samsung.com/global/sustainability/policy-file/AZTUlveqAMoALYMV/Samsung_Electronics_AI_Safety_Framework_en.pdf |
| 1.2.8 |
Making AI Inclusive – Four Guiding Principles for Ethical Engagement |
Partnership on AI |
https://partnershiponai.org/wp-content/uploads/dlm_uploads/2022/07/PAI_whitepaper_making-ai-inclusive.pdf |
| 1.2.9 |
Responsible Use of AI Guide |
Amazon Web Services (AWS) |
https://d1.awsstatic.com/products/generative-ai/responsbile-ai/AWS-Responsible-Use-of-AI-Guide-Final.pdf |
| 2 |
Concept-based Frameworks |
| 2.1 |
Accountability |
| 2.1.1 |
Raji, I. D., et al. (2020). Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing |
|
https://dl.acm.org/doi/pdf/10.1145/3351095.3372873 |
| 2.1.2 |
Cobbe, J. et al. (2021) Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems |
|
https://dl.acm.org/doi/pdf/10.1145/3442188.3445921 |
| 2.1.3 |
Guidance on the AI Auditing Framework – Draft guidance for consultation |
UK Information Commissioner’s Office |
https://ico.org.uk/media2/migrated/2617219/guidance-on-the-ai-auditing-framework-draft-for-consultation.pdf |
| 2.1.4 |
Artificial Intelligence – An Accountability Framework for Federal Agencies and Other Entities |
US Government Accountability Office (GAO) |
https://www.gao.gov/assets/gao-21-519sp.pdf |
| 2.10 |
Bias, Fairness, and Equity |
| 2.10.1 |
Bender, E. M., et al. (2018) Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science |
|
https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00041/43452/Data-Statements-for-Natural-Language-Processing |
| 2.10.2 |
Advancing Data Equity: An Action-Oriented Framework |
World Economic Forum |
https://www3.weforum.org/docs/WEF_Advancing_Data_Equity_2024.pdf |
| 2.11 |
Definitions, Terminology, and Classification |
| 2.11.1 |
OECD Framework for the Classification of AI Systems |
Organisation for Economic Co-operation and Development (OECD) |
https://www.oecd.org/content/dam/oecd/en/publications/reports/2022/02/oecd-framework-for-the-classification-of-ai-systems_336a8b57/cb6d9eca-en.pdf |
| 2.11.2 |
IEEE P3123 – Standard for Artificial Intelligence and Machine Learning (AI/ML) Terminology and Data Formats |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/3123/10744 |
| 2.11.3 |
IEEE 2802-2022 – IEEE Standard for Performance and Safety Evaluation of Artificial Intelligence Based Medical Devices: Terminology |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2802/7460 |
| 2.11.4 |
ETSI TS 104 050 V1.1.1 (2025-03) – Securing Artificial Intelligence (SAI); AI Threat Ontology and definitions |
European Telecommunications Standards Institute (ETSI) |
https://www.etsi.org/deliver/etsi_ts/104000_104099/104050/01.01.01_60/ts_104050v010101p.pdf |
| 2.11.5 |
ISO 8000-2:2022 – Data quality – Part 2: Vocabulary |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/85032.html |
| 2.11.6 |
ANSI/CTA-2089.1-2020 – Definitions/Characteristics of Artificial Intelligence in Health Care |
American National Standards Institute (ANSI) |
https://webstore.ansi.org/standards/ansi/ansicta20892020 |
| 2.12 |
Design, UI, and UX |
| 2.12.1 |
Weisz, J. D., et al. (2024). Design Principles for Generative AI Applications |
|
https://arxiv.org/pdf/2401.14484 |
| 2.12.2 |
Lee, K. (2025). Towards a Working Definition of Designing Generative User Interfaces |
|
https://arxiv.org/pdf/2505.15049 |
| 2.13 |
Embodied AI and Robotics |
| 2.13.1 |
Rachwal, K., et al. (2025). RAI: Flexible Agent Framework for Embodied AI |
|
https://arxiv.org/abs/2505.07532 |
| 2.14 |
Environmental Impact |
| 2.14.1 |
NWIP TR – Green and sustainable AI (N 256) |
The British Standards Institution (BSI) |
https://standardsdevelopment.bsigroup.com/projects/9022-07691 |
| 2.15 |
Explainability |
| 2.15.1 |
AI Explainability in Practice – Facilitator Workbook |
The Alan Turing Institute |
https://www.turing.ac.uk/sites/default/files/2024-06/aieg-ati-7-explainabilityv1.2.pdf |
| 2.15.2 |
Explaining Decisions Made with AI |
UK Information Commissioner’s Office |
https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/artificial-intelligence/explaining-decisions-made-with-artificial-intelligence |
| 2.15.3 |
Information Technology – Artificial Intelligence – Machine Learning (ML) model transparency |
Advanced Technology Academic Research Center (ATARC) |
https://atarc.org/project/information-technology-artificial-intelligence-machine-learning-ml-model-transparency |
| 2.15.4 |
IEEE P2976 – Standard for XAI - eXplainable Artificial Intelligence - for Achieving Clarity and Interoperability of AI Systems Design |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2976/10522 |
| 2.15.5 |
IEEE 2894-2024 – IEEE Guide for an Architectural Framework for Explainable Artificial Intelligence |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2894/11296 |
| 2.15.6 |
Chen, Y., et al. (2025). Policy Frameworks for Transparent Chain-of-Thought Reasoning in Large Language Models |
|
https://arxiv.org/pdf/2503.14521 |
| 2.16 |
Fairness and Bias |
| 2.16.1 |
ISO/IEC TR 24027:2021 – Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/77607.html |
| 2.16.2 |
Towards a Standard for Identifying and Managing Bias in Artificial Intelligence |
National Institute of Standards and Technology (NIST) |
https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.1270.pdf |
| 2.16.3 |
Confronting Bias: BSA’s Framework to Build Trust in AI |
The Software Alliance (BSA) |
https://www.nist.gov/system/files/documents/2021/08/23/ai-rmf-rfi-0045.pdf |
| 2.16.4 |
Purpose, Process, and Monitoring: A New Framework for Auditing Algorithmic Bias in Housing and Lending |
National Fair Housing Alliance (NFHA) |
https://nationalfairhousing.org/wp-content/uploads/2022/02/PPM_Framework_02_17_2022.pdf |
| 2.16.5 |
ISO/IEC TS 12791:2024 – Information technology — Artificial intelligence — Treatment of unwanted bias in classification and regression machine learning tasks |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/84110.html |
| 2.17 |
High Impact Risk |
| 2.17.1 |
Preparedness Framework (Beta) |
OpenAI |
https://cdn.openai.com/openai-preparedness-framework-beta.pdf |
| 2.17.2 |
Frontier AI Framework |
Meta |
https://ai.meta.com/static-resource/meta-frontier-ai-framework |
| 2.17.3 |
Responsible Scaling Policy (Version 2.1) |
Anthropic |
https://www-cdn.anthropic.com/17310f6d70ae5627f55313ed067afc1a762a4068.pdf |
| 2.18 |
Human-Computer Interaction |
| 2.18.1 |
Collaborations Between People and AI Systems (CPAIS): Human-AI Collaboration Framework and Case Studies |
Partnership on AI (PAI) |
https://partnershiponai.org/wp-content/uploads/2021/08/CPAIS-Framework-and-Case-Studies-9-23.pdf |
| 2.18.2 |
IEEE P7008 –Standard for Ethically Driven Nudging for Robotic, Intelligent and Autonomous Systems (Active PAR) |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/7008/7095 |
| 2.18.3 |
IEEE 7014-2024 – IEEE Standard for Ethical Considerations in Emulated Empathy in Autonomous and Intelligent Systems |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/7014/7648 |
| 2.18.4 |
IEEE 3128-2025 – IEEE Recommended Practice for the Evaluation of Artificial Intelligence (AI) Dialogue System Capabilities |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/3128/10746 |
| 2.18.5 |
IEEE 7010-2020 – IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/7010/7718 |
| 2.19 |
Impact Assessments |
| 2.19.1 |
The AI Impact Navigator – A guide for leaders to steward AI impact — socially, environmentally, and economically. |
Australian Government – Department of Industry, Science and Resources – National Artificial Intelligence Centre |
https://www.industry.gov.au/sites/default/files/2024-10/ai-impact-navigator-interactive.pdf |
| 2.19.2 |
ISO/IEC 42005:2025 -- Information technology — Artificial intelligence (AI) — AI system impact assessment |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/42005 |
| 2.19.3 |
Artificial Intelligence Impact Assessment |
ECP | Platform for the Information Society |
https://ecp.nl/wp-content/uploads/2019/01/Artificial-Intelligence-Impact-Assessment-English.pdf |
| 2.19.4 |
Human Rights AI Impact Assessment |
Law Commission of Ontario (LCO) & Ontario Human Rights Commission |
https://www3.ohrc.on.ca/sites/default/files/Human%20Rights%20Impact%20Assessment%20for%20AI.pdf |
| 2.2 |
Benchmarking and Performance |
| 2.2.1 |
ISO/IEC DTS 42119-2: Information technology – Artificial intelligence – Testing of AI – Part 2: Overview of testing AI systems |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/84127.html |
| 2.2.2 |
ISO/IEC NP TS 12831: Information technology – Artificial intelligence – Testing for AI Systems |
The British Standards Institution (BSI) |
https://standardsdevelopment.bsigroup.com/projects/9021-06406 |
| 2.2.3 |
IEEE 2937-2022: IEEE Standard for Performance Benchmarking for Artificial Intelligence Server Systems |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2937/10376 |
| 2.2.4 |
IEEE 2801-2022: IEEE Recommended Practice for the Quality Management of Datasets for Medical Artificial Intelligence |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2801/7459 |
| 2.20 |
Incident Response |
| 2.20.1 |
Deployment Corrections – An incident response framework for frontier AI models |
Institute for AI Policy and Strategy (IAPS) |
https://arxiv.org/pdf/2310.00328 |
| 2.21 |
Licensing |
| 2.21.1 |
IEEE 2840-2024 – IEEE Standard for Responsible AI Licensing |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2840/7673 |
| 2.22 |
Robustness |
| 2.22.1 |
IEEE 3129-2023 – IEEE Standard for Robustness Testing and Evaluation of Artificial Intelligence (AI)-based Image Recognition Service |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/3129/10747 |
| 2.22.2 |
ISO/IEC TR 24029-1:2021 – Artificial Intelligence (AI) — Assessment of the robustness of neural networks – Part 1: Overview |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/77609.html |
| 2.23 |
System Management |
| 2.23.1 |
ISO/IEC 42001:2023 – Information technology — Artificial intelligence — Management system |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/42001 |
| 2.23.2 |
Smith, C. J. (2019). Designing Trustworthy AI: A Human-Machine Teaming Framework to Guide Development |
|
https://arxiv.org/ftp/arxiv/papers/1910/1910.03515.pdf |
| 2.23.3 |
ISO/IEC TS 25058:2024 – Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Guidance for quality evaluation of artificial intelligence (AI) systems |
International Organization for Standardization (ISO) |
|
| 2.23.4 |
ISO/IEC 5338:2023 – Information technology — Artificial intelligence — AI system life cycle processes |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/81118.html |
| 2.23.5 |
ISO/IEC 38507:2022 -- Information technology — Governance of IT — Governance implications of the use of artificial intelligence by organizations |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/56641.html |
| 2.24 |
Transparency |
| 2.24.1 |
ISO/IEC FDIS 12792 – Information technology — Artificial intelligence — Transparency taxonomy of AI systems |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/84111.html |
| 2.24.2 |
IEEE 7001-2021 – IEEE Standard for Transparency of Autonomous Systems |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/7001/6929 |
| 2.24.3 |
Bennet, K., et al. (2024). Implementing AI Bill of Materials (AI BOM) with SPDX 3.0 |
|
https://arxiv.org/pdf/2504.16743 |
| 2.25 |
Trustworthiness |
| 2.25.1 |
Ethics guidelines for trustworthy AI |
European Commission |
https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai |
| 2.25.2 |
Baker-Brunnbauer, J. (2021). TAII Framework for Trustworthy AI Systems |
|
https://journal.robonomics.science/index.php/rj/article/view/17/6 |
| 2.25.3 |
ANSI/CTA-2090 – The Use of Artificial Intelligence in Health Care: Trustworthiness |
American National Standards Institute (ANSI) |
https://shop.cta.tech/products/cta-2090 |
| 2.25.4 |
ISO/IEC TR 24028:2020 – Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/77608.html |
| 2.25.5 |
ANSI/CTA 2096 – Guidelines for Developing Trustworthy AI Systems |
American National Standards Institute (ANSI) |
https://shop.cta.tech/products/cta-2096 |
| 2.25.6 |
VDE-AR-E 2842-61-6 Anwendungsregel:2021-06 – Development and trustworthiness of autonomous/cognitive systems |
VDE VERLAG GmbH |
https://www.vde-verlag.de/standards/0800732/vde-ar-e-2842-61-6-anwendungsregel-2021-06.html |
| 2.26 |
Validation |
| 2.26.1 |
BS 30440:2023 – Validation framework for the use of artificial intelligence (AI) within healthcare. Specification |
The British Standards Institution (BSI) |
https://standardsdevelopment.bsigroup.com/projects/2021-00605#/section |
| 2.26.2 |
ISO/IEC DTS 42119-3 – Artificial intelligence — Testing of AI – Part 3: Verification and validation analysis of AI systems (under development) |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/85072.html |
| 2.3 |
Agentic Systems |
| 2.3.1 |
Lanus, E., et al. (2021). Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents |
|
https://doi.org/10.1109/SOSE52739.2021.9497472 |
| 2.3.2 |
Responsible bots: 10 guidelines for developers of conversational AI |
Microsoft (~221,000 employees) |
https://www.microsoft.com/en-us/research/wp-content/uploads/2018/11/Bot_Guidelines_Nov_2018.pdf |
| 2.3.3 |
Shavit, Y., et al. (n.d.). Practices for Governing Agentic AI Systems |
|
https://cdn.openai.com/papers/practices-for-governing-agentic-ai-systems.pdf |
| 2.3.4 |
Chan, A., et al. (2024). Visibility into AI Agents |
|
https://arxiv.org/pdf/2401.13138 |
| 2.3.5 |
AI Coding Assistants |
Federal Office for Information Security |
https://www.bsi.bund.de/SharedDocs/Downloads/EN/BSI/KI/ANSSI_BSI_AI_Coding_Assistants.pdf |
| 2.3.6 |
Ognibene, D., et al. (2025) SCOOP: A Framework for Proactive Collaboration and Social Continual Learning through Natural Language Interaction and Causal Reasoning |
|
https://arxiv.org/pdf/2503.10241 |
| 2.3.7 |
Ranjan, R., et al. (2025) LOKA Protocol: A Decentralized Framework for Trustworthy and Ethical AI Agent Ecosystems |
|
https://arxiv.org/pdf/2504.10915 |
| 2.3.8 |
Liu, J., et al. (2025). ACPs: Agent Collaboration Protocols for the Internet of Agents |
|
https://arxiv.org/abs/2505.13523 |
| 2.4 |
Content Provenance |
| 2.4.1 |
C2PA Specifications |
Coalition for Content Provenance and Authenticity (C2PA) |
https://c2pa.org/specifications/specifications/2.0/index.html |
| 2.5 |
Cybersecurity and Safety |
| 2.5.1 |
Voluntary AI Safety Standard |
Australian Government – Department of Industry, Science and Resources – National Artificial Intelligence Centre |
https://www.industry.gov.au/sites/default/files/2024-09/voluntary-ai-safety-standard.pdf |
| 2.5.10 |
ISO/IEC TS 27022:2021: Information technology – Guidance on information security management system processes |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/61004.html |
| 2.5.11 |
NIST AI 100-2e2023: NIST Trustworthy and Responsible AI: Vassilev, A., et al. (2023). Adversarial Machine Learning – A Taxonomy and Terminology of Attacks and Mitigations |
National Institute of Standards and Technology (NIST) |
https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf |
| 2.5.12 |
2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps |
Open Web Application Security Project (OWASP) |
https://genai.owasp.org/llm-top-10 |
| 2.5.13 |
AI Cyber Security Code of Practice |
UK Government – Department for Science, Innovation & Technology |
https://www.gov.uk/government/calls-for-evidence/cyber-security-of-ai-a-call-for-views/a-call-for-views-on-the-cyber-security-of-ai#ai-cyber-security-code-of-practice |
| 2.5.14 |
Guidelines and Companion Guide on Securing AI Systems |
Cyber Security Agency (CSA) of Singapore |
https://www.csa.gov.sg/resources/publications/guidelines-and-companion-guide-on-securing-ai-systems |
| 2.5.15 |
Databricks AI Security Framework (DASF) 2.0 – An actionable framework for managing AI security |
Databricks |
https://www.databricks.com/resources/whitepaper/databricks-ai-security-framework-dasf |
| 2.5.2 |
Guidelines on Securing AI Systems |
Cyber Security Agency of Singapore |
https://isomer-user-content.by.gov.sg/36/e05d8194-91c4-4314-87d4-0c0e013598fc/Guidelines%20on%20Securing%20AI%20Systems.pdf |
| 2.5.3 |
AI Safety Governance Framework |
China’s National Cyber Security Standardization Technical Committee Secretariat |
https://www.tc260.org.cn/upload/2024-09-09/1725849192841090989.pdf |
| 2.5.4 |
ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) |
The MITRE Corporation |
https://atlas.mitre.org |
| 2.5.5 |
Presidio AI Framework: Towards Safe Generative AI Models |
World Economic Forum |
https://www3.weforum.org/docs/WEF_Presidio_AI%20Framework_2024.pdf |
| 2.5.6 |
Smith, C., et al. (n.d.) Hazard Contribution Modes of Machine Learning Components |
|
https://ntrs.nasa.gov/api/citations/20200001851/downloads/20200001851.pdf |
| 2.5.7 |
IEEE 7009-2024: IEEE Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/7009/7096 |
| 2.5.8 |
ETSI GR SAI 006 V1.1.1 (2022-03): Securing Artificial Intelligence (SAI); The role of hardware in security of AI |
Secure AI (SAI) ETSI Industry Specification Group (ISG) |
https://cdn.standards.iteh.ai/samples/60132/b0afcc3e17f54ee4b7e724e5670b26dc/ETSI-GR-SAI-006-V1-1-1-2022-03-.pdf |
| 2.5.9 |
IEEE P3157: Recommended Practice for Vulnerability Test for Machine Learning Models for Computer Vision Applications |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/3157/10876 |
| 2.6 |
Red Teaming |
| 2.6.1 |
Guide to Red Teaming Methodology on AI Safety |
Japan AI Safety Institute (AISI) |
https://aisi.go.jp/assets/pdf/ai_safety_RT_v1.00_en.pdf |
| 2.6.2 |
Red Teaming for GenAI Harms – Revealing the Risks and Rewards for Online Safety |
Ofcom |
https://www.ofcom.org.uk/siteassets/resources/documents/consultations/discussion-papers/red-teaming/red-teaming-for-gen-ai-harms.pdf |
| 2.6.3 |
Walter, M. J., et al. (2024) A Red Teaming Framework for Securing AI in Maritime Autonomous Systems |
|
https://www.tandfonline.com/doi/epdf/10.1080/08839514.2024.2395750 |
| 2.6.4 |
Artificial Intelligence Safety Commitments |
China Academy of Information and Communications Technology (CAICT) |
https://mp.weixin.qq.com/s/s-XFKQCWhu0uye4opgb3Ng |
| 2.7 |
Retrieval Systems |
| 2.7.1 |
Ammann, L., et al. (2025). Securing RAG: A Risk Assessment and Mitigation Framework |
|
https://arxiv.org/pdf/2505.08728 |
| 2.8 |
Data |
| 2.8.1 |
ISO/IEC 5259-4:2024 - Artificial intelligence - Data quality for analytics and machine learning (ML) – Part 4: Data quality process framework |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/81093.html |
| 2.8.2 |
Afzal, S., et al. (2021) Data Readiness Report |
|
https://ieeexplore.ieee.org/abstract/document/9592479 |
| 2.8.3 |
Hutchinson, B., et al. (2025) Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure |
|
https://dl.acm.org/doi/pdf/10.1145/3442188.3445918 |
| 2.8.4 |
Data Provenance Standard |
Data & Trust Alliance |
https://dataandtrustalliance.org/work/data-provenance-standards |
| 2.8.5 |
Faridoon, A., et al. (2024). Healthcare Data Governance, Privacy, and Security – A Conceptual Framework |
|
https://arxiv.org/pdf/2403.17648 |
| 2.8.6 |
ISO/IEC 25012:2008 – Software engineering – Software product Quality Requirements and Evaluation (SQuaRE) – Data quality model |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/35736.html |
| 2.8.7 |
ISO/IEC 25024:2015 – Systems and software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) - Measurement of data quality |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/35749.html |
| 2.8.8 |
Privacy Enhancing Technology (PET): Proposed Guide on Synthetic Data Generation |
Agency for Science, Technology and Research and Personal Data Protection Commission (PDPC) Singapore |
https://www.pdpc.gov.sg/-/media/files/pdpc/pdf-files/other-guides/proposed-guide-on-synthetic-data-generation.pdf |
| 2.8.9 |
Baack, S. et al. (2025). Dataset Convening -Towards Best Practices for Open Datasets for LLM Training |
|
https://arxiv.org/pdf/2501.08365 |
| 2.9 |
Annotation/Labelling |
| 2.9.1 |
Best Practices for Managing Data Annotation Projects |
Bloomberg (>21,000 employees) |
https://assets.bbhub.io/company/sites/40/2020/09/Annotation-Best-Practices-091020-FINAL.pdf |
| 2.9.2 |
Data Enrichment Sourcing Guidelines |
Partnership on AI |
https://partnershiponai.org/wp-content/uploads/2022/11/data-enrichment-guidelines.pdf |
| 2.9.3 |
Measures for Labeling Synthetic Content Generated by Artificial Intelligence |
Office of the Central Cyberspace Affairs Commission – Cyberspace Administration of China (CAC) |
https://www.cac.gov.cn/2025-03/14/c_1743654684782215.htm |
| 2.9.4 |
Cybersecurity technology – Labeling method for content generated by artificial intelligence |
China’s National Cyber Security Standardization Technical Committee Secretariat |
https://www.tc260.org.cn/upload/2025-03-15/1742009439794081593.pdf |
| 3 |
Industry-based Frameworks |
| 3.1 |
Cognitive Technology |
| 3.1.1 |
Cognitive Project Management in AI (CPMAI)™ v7 - Training & Certification |
Project Management Institute |
https://www.pmi.org/shop/p-/digital-product/cognitive-project-management-in-ai-(cpmai)-v7---training-,-a-,-certification/cpmai-b-01 |
| 3.2 |
Education and Academia |
| 3.2.1 |
Guidance for generative AI in education and research |
United Nations Educational, Scientific and Cultural Organization (UNESCO) |
https://unesdoc.unesco.org/ark:/48223/pf0000386693/PDF/386693eng.pdf.multi.page=1 |
| 3.2.2 |
IEEE P2247.4 – IEEE Draft Recommended Practice for Ethically Aligned Design of Artificial Intelligence (AI) in Adaptive Instructional Systems (Active PAR) |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2247.4/10368 |
| 3.2.3 |
Mann, S. P., et al. (2024). Guidelines for ethical use and acknowledgement of large language models in academic writing |
|
https://www.nature.com/articles/s42256-024-00922-7.epdf?sharing_token=HuXgch8N4TMiIj__TuNSv9RgN0jAjWel9jnR3ZoTv0P3hemWIDPPHmWTcywtbB85sAqgSgUPlEd4rvaS_JR2nwptkIduhXOJnYw13H3HPUNTaIR45uYNT79ia82sOi6bSXQu-cl578SzVjPf3cfCt6rXJuSmUnAtRhz0T97rYaE%3D |
| 3.3 |
Energy |
| 3.3.1 |
Generative Artificial Intelligence Reference Guide |
US Department of Energy |
https://www.energy.gov/sites/default/files/2024-12/Generative%20AI%20Reference%20Guide%20v2%206-14-24.pdf |
| 3.4 |
Healthcare and Pharmaceuticals |
| 3.4.1 |
Artificial Intelligence (AI) Ethics Principles – Principles to guide ethical AI use |
Roche |
https://assets.roche.com/f/176343/x/401c28049f/roche-ai-ethics-principles.pdf |
| 3.4.2 |
Ethics and governance of artificial intelligence for health – Guidance on large multi-modal models |
World Health Organization (WHO) |
https://iris.who.int/bitstream/handle/10665/375579/9789240084759-eng.pdf |
| 3.4.3 |
IEC SRD 63416:2023 ED1 – Ethical considerations of artificial intelligence (AI) when applied in the active assisted living (AAL) context |
International Electrotechnical Commission (IEC) |
https://www.iec.ch/ords/f?p=103:38:209091949154989::::FSP_ORG_ID,FSP_APEX_PAGE,FSP_PROJECT_ID:11827,23,103371 |
| 3.5 |
Intelligence |
| 3.5.1 |
Artificial Intelligence Ethics Framework for the Intelligence Community |
Office of the Director of National Intelligence (US) |
https://www.intelligence.gov/ai/ai-ethics-framework |
| 3.5.2 |
Principles of Artificial Intelligence Ethics for the Intelligence Community |
Office of the Director of National Intelligence (US) |
https://www.intelligence.gov/assets/documents/pdf/ai/principles-of-ai-ethics-for-the-ic.pdf |
| 3.6 |
Legal Services |
| 3.6.1 |
Draft UNESCO Guidelines for the Use of AI Systems in Courts and Tribunals |
United Nations Educational, Scientific and Cultural Organization (UNESCO) |
https://unesdoc.unesco.org/ark:/48223/pf0000390781 |
| 3.7 |
Media |
| 3.7.1 |
PAI’s Responsible Practices for Synthetic Media – A Framework for Collective Action |
Partnership on AI (PAI) |
https://syntheticmedia.partnershiponai.org |
| 3.7.2a |
AI and Responsible Journalism Toolkit: Education |
Desirable.AI (Leverhulme Centre for the Future of Intelligence – University of Cambridge, UK, and Center for Science and Thought – University of Bonn, Germany |
https://www.desirableai.com/journalism-toolkit-edu |
| 3.7.2b |
AI and Responsible Journalism Toolkit: Ethics/Policy |
Desirable.AI (Leverhulme Centre for the Future of Intelligence – University of Cambridge, UK, and Center for Science and Thought – University of Bonn, Germany |
https://www.desirableai.com/journalism-toolkit-ethics |
| 3.7.2c |
AI and Responsible Journalism Toolkit: Voices |
Desirable.AI (Leverhulme Centre for the Future of Intelligence – University of Cambridge, UK, and Center for Science and Thought – University of Bonn, Germany |
https://www.desirableai.com/journalism-toolkit-voices |
| 3.7.2d |
AI and Responsible Journalism Toolkit: Structures |
Desirable.AI (Leverhulme Centre for the Future of Intelligence – University of Cambridge, UK, and Center for Science and Thought – University of Bonn, Germany |
https://www.desirableai.com/journalism-toolkit-orgs |
| 3.7.3 |
AI Manifesto |
Blue Zoo Media Group Ltd (London, UK) |
https://www.blue-zoo.co.uk/policies/ai-manifesto |
| 3.8 |
Psychology and Mental Health |
| 3.8.1 |
Steenstra, I., et al. (2025) A Risk Taxonomy for Evaluating AI-Powered Psychotherapy Agents |
|
https://arxiv.org/pdf/2505.15108 |
| 3.9 |
Public sector/Government |
| 3.9.1 |
Policy for the responsible use of AI in government (Version 1.1) |
Australian Government – Digital Transformation Agency |
https://www.digital.gov.au/sites/default/files/documents/2024-08/Policy%20for%20the%20responsible%20use%20of%20AI%20in%20government%20v1.1.pdf |
| 3.9.2 |
Engin, Z., et al. (2025) The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government |
|
https://arxiv.org/pdf/2503.08725 |
| 4 |
Role-based Frameworks |
| 4.1 |
Investors |
| 4.1.1 |
RESPONSIBLE AI STARTUPS (RAIS) Framework |
Radical Ventures |
https://github.com/radicalventures/RAIS-Framework |
| 4.2 |
Boards |
| 4.2.1 |
AI Governance Framework for Boards |
Anekanta AI |
https://anekanta.co.uk/ai-governance-and-compliance/anekanta-responsible-ai-governance-framework-for-boards |
| 4.3 |
Startup Founders |
| 4.4 |
Responsible Innovation Labs |
| 4.4.1a |
Responsible AI Framework 101 – Pre-product and/or raised a Pre-seed or Seed |
Responsible Innovation Labs (RIL) |
https://docs.google.com/presentation/d/e/2PACX-1vSHVxh-HtpzXH1z_PmPJturxV9fXMbhvE6NjJuZLu3FFtmKrC-aDHV66mKF9yLe4eCT7UDmMLuuI7GA/pub?start=false&loop=false&delayms=3000&slide=id.g2ef4f43a125_0_152 |
| 4.4.1b |
Responsible AI Framework 201 – Post-product and raised a Seed or Series A |
Responsible Innovation Labs (RIL) |
https://docs.google.com/presentation/d/e/2PACX-1vR337zZUkx9FwWGepmh3UaoQ5pYNLpKcESVNADR76mADhWg0nw4trS0Wwxi5u1-hG7PJBjHGRN9DEeY/pub?start=false&loop=false&delayms=3000&slide=id.g2ef4f7411db_0_116 |
| 4.4.1c |
Responsible AI Framework 301 – Scaling and raised a Series B or more |
Responsible Innovation Labs (RIL) |
https://docs.google.com/presentation/d/e/2PACX-1vSAOvD1N8Rx6MR89rD2p--UsXjT9XzljhU9nVZQ7QGLIvel8qf93KKe1V__uih70v4HJyYxhjmhLTUW/pub?start=false&loop=false&delayms=3000&slide=id.g2ef4f74123a_0_113 |
| 4.4.2 |
Responsible AI Framework v2 (Scaling and raised a Series B or more) |
Responsible Innovation Labs (RIL) |
https://www.rilabs.org/responsible-ai#RAI-v2 |
| 4.5 |
Leadership and Executives |
| 4.5.1 |
Empowering AI Leadership – An Oversight Toolkit for Boards of Directors |
World Economic Forum |
https://express.adobe.com/page/RsXNkZANwMLEf |
| 4.5.2 |
IEEE P2863 – Recommended Practice for Organizational Governance of Artificial Intelligence (Active PAR) |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/2863/10142 |
| 4.5.3 |
XP Z77-101 – Guide of good practices in matters of governance of ethical approaches within organizations |
Afnor Editions |
https://www.boutique.afnor.org/en-gb/standard/xp-z77101/guide-of-good-practices-in-matters-of-governance-of-ethical-approaches-with/fa200187/263408 |
| 4.6 |
Information Technology |
| 4.6.1 |
ISO/IEC TS 38501:2015 – Information technology — Governance of IT — Implementation guide |
International Organization for Standardization (ISO) |
https://www.iso.org/standard/45263.html |
| 4.7 |
Procurement |
| 4.7.1 |
IEEE 3119-2025 – IEEE Standard for the Procurement of Artificial Intelligence and Automated Decision Systems |
Institute of Electrical and Electronics Engineers Standards Association (IEEE SA) |
https://standards.ieee.org/ieee/3119/10729 |
| 4.7.2 |
AFNOR SPEC Z77-100-0 - Contractualization of AI systems |
|
|
| 4.8 |
Human Resources |
| 4.8.1 |
Guidelines for AI and Shared Prosperity – Tools for improving AI’s impact on jobs |
Partnership on AI (PAI) |
https://partnershiponai.org/wp-content/uploads/dlm_uploads/2023/06/pai_guidelines_shared_prosperity.pdf |
| 4.8.2 |
Framework for Promoting Workforce Well-being in the AI-Integrated Workplace |
Partnership on AI (PAI) |
https://partnershiponai.org/download/4059 |
| 4.9 |
Marketing & Advertising |
| 4.9.1 |
ANA Ethics Code of Marketing Best Practices – Digital Innovation (AI, Machine Learning, and Automated Processing) |
Association of National Advertisers (ANA) |
https://www.ana.net/content/show/id/accountability-chan-ethicscode-final#bookmark52 |
RAISEF
