mlsanityData to 7 Oct 2026

Responsible Reporting for Frontier AI Development

arXiv 2404.02675 ↗First posted 3 Apr 2024cs.CY cs.AIcited by 10

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Abstract (arXiv)

Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 36 of its 90 references that are in the map. First posted on arXiv 3 Apr 2024.

Builds on

36 of 90 references.

References · 36

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
  3. 2022Emergent Abilities of Large Language Modelscited by 2,483
  4. 2018Datasheets for Datasetscited by 1,371
  5. 2018Model Cards for Model Reportingcited by 1,068
  6. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
  7. 2021Unsolved Problems in ML Safetycited by 291
  8. 2023An Overview of Catastrophic AI Riskscited by 214
  9. 2022Predictability and Surprise in Large Generative Modelscited by 209
  10. 2023Model evaluation for extreme riskscited by 167
  11. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
  12. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
24 more
  1. 2023Auditing large language models: a three-layered approachcited by 135
  2. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 123
  3. 2024Evaluating Frontier Models for Dangerous Capabilitiescited by 121
  4. 2020Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Databasecited by 95
  5. 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
  6. 2022Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governancecited by 67
  7. 2024On the Societal Impact of Open Foundation Modelscited by 64
  8. 2023Managing extreme AI risks amid rapid progresscited by 48
  9. 2024A Safe Harbor for AI Evaluation and Red Teamingcited by 46
  10. 2023The History and Risks of Reinforcement Learning and Human Feedbackcited by 42
  11. 2024AI auditing: The Broken Bus on the Road to AI Accountabilitycited by 42
  12. 2024Computing Power and the Governance of Artificial Intelligencecited by 38
  13. 2023Ecosystem Graphs: The Social Footprint of Foundation Modelscited by 31
  14. 2023Towards best practices in AGI safety and governance: A survey of expert opinioncited by 27
  15. 2024Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Toolingcited by 27
  16. 2019The Role of Cooperation in Responsible AI Developmentcited by 21
  17. 2022System Safety and Artificial Intelligencecited by 18
  18. 2022Reward Reports for Reinforcement Learningcited by 18
  19. 2021Filling gaps in trustworthy development of AIcited by 14
  20. 2021Why and How Governments Should Monitor AI Developmentcited by 13
  21. 2023Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providerscited by 12
  22. 2023Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Frameworkcited by 11
  23. 2024Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulationcited by 9
  24. 2022Indexing AI Risks with Incidents, Issues, and Variantscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 4.

10 papers.

  1. 2024The 2024 Foundation Model Transparency Indexcited by 18
  2. 2024Societal Adaptation to Advanced AIcited by 10
  3. 2025The 2025 Foundation Model Transparency Indexcited by 9
  4. 2025International Agreements on AI Safety: Review and Recommendations for a Conditional AI Safety Treatycited by 3
  5. 2025NeurIPS should lead scientific consensus on AI policycited by 3
  6. 2024Standardization Trends on Safety and Trustworthiness Technology for Advanced AIcited by 0
  7. 2026Incentive Aware AI Regulations: A Credal Characterisationcited by 0
  8. 2026NeurIPS Should Require Reproducibility Standards for Frontier AI Safety Claimscited by 0
  9. 2026Plausible Deniability Guarantees for Whistleblowerscited by 0
  10. 2026Silent Revision: Measuring Undisclosed Change in the Safety Frameworks of Frontier AI Developerscited by 0