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Responsible Reporting for Frontier AI Development (2404.02675v1)

Published 3 Apr 2024 in cs.CY and cs.AI

Abstract: 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.

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Responsible Reporting for Frontier AI Development

Introduction to Responsible Reporting

The continuous advancement of AI systems, particularly frontier AI systems, poses new challenges and risks that regulatory bodies, researchers, and organizations need to manage effectively. The report on "Responsible Reporting for Frontier AI Development" explores the mechanisms through which organizations developing and deploying these innovative AI systems can report safety-critical information. This information sharing is aimed at enabling more informed decision-making processes among developers, policymakers, and broader stakeholders regarding the governance and risk management of these systems.

The Need for Reporting

The report underlines the essence of sharing detailed and up-to-date information on the risks associated with frontier AI systems. This sharing of information is vital for fostering awareness among developers, regulators, and civil society about the potential societal impacts of these technologies. Moreover, it emphasizes that through responsible reporting, organizations can incentivize the adoption of better risk management and safety practices across the industry. Additionally, enhancing the regulatory visibility into the capabilities and deployment contexts of these AI systems facilitates the crafting of effective regulatory frameworks and policies.

Reporting Goals and Objectives

  • Risk Awareness: The documentation underscores the importance of understanding the risks and vulnerabilities of frontier AI systems to mitigate them effectively.
  • Improving Industry Practices: By sharing information on risks and effective mitigation strategies, organizations can collectively uplift their safety protocols and practices.
  • Enhancing Regulatory Visibility: Provides government bodies and policymakers with crucial data, aiding them in developing well-informed regulatory measures targeted at newly emerging risks.

Decision-Relevant Information

The paper categorizes the types of information that could be shared into development and deployment, risks and harms, and mitigations. Each category is designed to equip the relevant stakeholders with the information necessary to make informed decisions on technical, organizational, and policy responses to the capabilities and risks presented by frontier AI systems. This systematic categorization aligns with contemporary regulatory initiatives and proposals, like the EU's AI Act and the U.S. executive order on AI.

Institutional Framework for Reporting

The proposed reporting framework encourages participation from a broad range of stakeholders, including industry players, government entities, and independent domain experts. It outlines the roles of these groups in contributing to, receiving, and utilizing the reported information. The report suggests mechanisms like differential disclosure and anonymized reporting to address challenges related to intellectual property and reputational risks. Additionally, it introduces the idea of a reciprocity principle, where only those developers who contribute information would have access to shared insights, thus incentivizing participation.

Implementation Challenges and Solutions

The paper recognises several challenges to the effective implementation of the proposed reporting framework, ranging from protecting intellectual property to ensuring accurate and complete reporting. It offers pathways to address these challenges through both voluntary and regulatory means, suggesting targeted institutional mechanisms to alleviate developers' concerns and institutional capacity building for government bodies.

Conclusion and Future Directions

The report concludes by stressing the significance of responsible reporting as a means to enhance the safety and governance of frontier AI technologies. It emphasizes that while challenges exist, there are feasible pathways to implementing a reporting framework that benefits all stakeholders. Future developments should focus on refining these frameworks to address evolving risks and enhancing collaboration among developers, policymakers, and experts in the field.

Acknowledgements

The paper concludes with gratitude towards various contributors and experts who provided feedback and insights, highlighting the collaborative effort involved in pushing the boundaries of AI safety and governance.

In summary, "Responsible Reporting for Frontier AI Development" presents a thorough and well-structured approach to enhancing AI safety and governance through systematic and responsible information sharing. As the AI landscape continues to evolve, the principles and recommendations outlined in this report can serve as a cornerstone for future efforts to manage the risks associated with these powerful technologies responsibly.

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References (90)
  1. AI Policy and Governance Working Group “Comment of the AI Policy and Governance Working Group on the NTIA AI Accountability Policy Request for Comment Docket NTIA-230407-0093”, 2023 URL: https://www.ias.edu/sites/default/files/AI%20Policy%20and%20Governance%20Working%20Group%20NTIA%20Comment.pdf
  2. “Frontier AI Regulation: Managing Emerging Risks to Public Safety” arXiv, 2023 DOI: 10.48550/arXiv.2307.03718
  3. “Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework” arXiv, 2023 DOI: 10.48550/arXiv.2311.14711
  4. “Responsible Scaling: Comparing Government Guidance and Company Policy”, 2024 URL: https://www.iaps.ai/research/responsible-scaling
  5. Anthropic “Anthropic’s Responsible Scaling Policy”, 2023 URL: https://www.anthropic.com/news/anthropics-responsible-scaling-policy
  6. Yonathan A. Arbel, Matthew Tokson and Albert Lin “Systemic Regulation of Artificial Intelligence” In Arizona State Law Journal, AI Safety Legal Paper Series 12-24 URL: https://ssrn.com/abstract=4666854
  7. “AI Accidents: An Emerging Threat”, 2021 URL: https://cset.georgetown.edu/publication/ai-accidents-an-emerging-threat/
  8. Amanda Askell, Miles Brundage and Gillian Hadfield “The Role of Cooperation in Responsible AI Development” arXiv, 2019 DOI: 10.48550/arXiv.1907.04534
  9. “Filling gaps in trustworthy development of AI” Publisher: American Association for the Advancement of Science In Science 374.6573, 2021, pp. 1327–1329 DOI: 10.1126/science.abi7176
  10. “Managing AI Risks in an Era of Rapid Progress” arXiv, 2023 DOI: 10.48550/arXiv.2310.17688
  11. “AI auditing: The Broken Bus on the Road to AI Accountability” arXiv, 2024 DOI: 10.48550/arXiv.2401.14462
  12. “The Foundation Model Transparency Index” arXiv, 2023 DOI: 10.48550/arXiv.2310.12941
  13. “Ecosystem Graphs” URL: https://crfm.stanford.edu/ecosystem-graphs/index.html?mode=table
  14. “Ecosystem Graphs: The Social Footprint of Foundation Models” arXiv, 2023 DOI: 10.48550/arXiv.2303.15772
  15. “Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims” arXiv, 2020 DOI: 10.48550/arXiv.2004.07213
  16. “Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback” arXiv, 2023 DOI: 10.48550/arXiv.2307.15217
  17. “Black-Box Access is Insufficient for Rigorous AI Audits” arXiv, 2024 DOI: 10.48550/arXiv.2401.14446
  18. CFPB “CFPB Supervision and Examination Process”, 2022 URL: https://files.consumerfinance.gov/f/documents/cfpb_supervision-and-examination-manual.pdf
  19. Jack Clark “Information Markets and AI Development” In The Oxford Handbook of AI Governance Oxford University Press, 2023 DOI: 10.1093/oxfordhb/9780197579329.013.21
  20. Allan Dafoe “AI Governance: Overview and Theoretical Lenses” In The Oxford Handbook of AI Governance Oxford University Press, 2023 DOI: 10.1093/oxfordhb/9780197579329.013.2
  21. Department for Science, Innovation & Technology “Introducing the AI Safety Institute” URL: https://www.gov.uk/government/publications/ai-safety-institute-overview/introducing-the-ai-safety-institute
  22. Department for Science, Innovation and Technology “Policy paper: Emerging processes for frontier AI safety”, 2023 URL: https://www.gov.uk/government/publications/emerging-processes-for-frontier-ai-safety/emerging-processes-for-frontier-ai-safety
  23. Department for Science, Innovation and Technology and AI Safety Institute “Policy Updates”, 2023 URL: https://www.aisafetysummit.gov.uk/policy-updates/
  24. “Tech entrepreneur Ian Hogarth to lead UK’s AI Foundation Model Taskforce”, 2023 URL: https://www.gov.uk/government/news/tech-entrepreneur-ian-hogarth-to-lead-uks-ai-foundation-model-taskforce
  25. “The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023”, 2023 URL: https://www.gov.uk/government/publications/ai-safety-summit-2023-the-bletchley-declaration/the-bletchley-declaration-by-countries-attending-the-ai-safety-summit-1-2-november-2023
  26. Roel I.J. Dobbe “System Safety and Artificial Intelligence” In The Oxford Handbook of AI Governance Oxford University Press, 2022 DOI: 10.1093/oxfordhb/9780197579329.013.67
  27. “Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers” arXiv, 2023 DOI: 10.48550/arXiv.2310.13625
  28. EPOCH “Epoch Database”, 2024 URL: https://epochai.org/data/epochdb/table
  29. European Parliament “2021/0106(COD): Artificial Intelligence Act” URL: https://oeil.secure.europarl.europa.eu/oeil/popups/ficheprocedure.do?reference=2021/0106(COD)&l=en
  30. “Governing AI safety through independent audits” Publisher: Nature Publishing Group In Nature Machine Intelligence 3.7, 2021, pp. 566–571 DOI: 10.1038/s42256-021-00370-7
  31. Federal Aviation Administration “Mandatory and Voluntary Incident Reporting”, 2023 URL: https://www.faa.gov/hazmat/incident-reporting
  32. Federal Aviation Administration “The Inspection Process”, 2023 URL: https://www.faa.gov/hazmat/safecargo/why_am_i_being_inspected/inspection_process
  33. “Adding Structure to AI Harm”, 2023 URL: https://cset.georgetown.edu/publication/adding-structure-to-ai-harm/
  34. Frontier Model Forum “Frontier Model Forum: Advancing frontier AI safety” URL: https://www.frontiermodelforum.org/
  35. “Predictability and Surprise in Large Generative Models” In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22 New York, NY, USA: Association for Computing Machinery, 2022, pp. 1747–1764 DOI: 10.1145/3531146.3533229
  36. “Datasheets for Datasets” arXiv, 2021 DOI: 10.48550/arXiv.1803.09010
  37. “Gemini: A Family of Highly Capable Multimodal Models” arXiv, 2023 DOI: 10.48550/arXiv.2312.11805
  38. “Reward Reports for Reinforcement Learning” In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’23 New York, NY, USA: Association for Computing Machinery, 2023, pp. 84–130 DOI: 10.1145/3600211.3604698
  39. Google DeepMind “AI Safety Summit: An update on our approach to safety and responsibility”, 2023 URL: https://deepmind.google/public-policy/ai-summit-policies/
  40. “AI Regulation Has Its Own Alignment Problem: The Technical and Institutional Feasibility of Disclosure, Registration, Licensing, and Auditing” In The George Washington Law Review 92, 2024 URL: https://dho.stanford.edu/wp-content/uploads/AI_Regulation.pdf
  41. Gillian Hadfield, Mariano Florentino Cuéllar and Tim O’Reilly “It’s Time to Create a National Registry for Large AI Models”, 2023 URL: https://carnegieendowment.org/2023/07/12/it-s-time-to-create-national-registry-for-large-ai-models-pub-90180
  42. “Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation” arXiv, 2024 DOI: 10.48550/arXiv.2403.08501
  43. “Unsolved Problems in ML Safety” arXiv, 2022 DOI: 10.48550/arXiv.2109.13916
  44. Dan Hendrycks, Mantas Mazeika and Thomas Woodside “An Overview of Catastrophic AI Risks” arXiv, 2023 DOI: 10.48550/arXiv.2306.12001
  45. Margot E. Kaminski “Regulating the Risks of AI” In Boston University Law Review 103, 2023, pp. 1347–1411 URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4195066
  46. “On the Societal Impact of Open Foundation Models”, 2024 URL: https://crfm.stanford.edu/open-fms/paper.pdf
  47. Bradley Karkkainen “Bottlenecks and baselines: Tackling information deficits in environmental regulation” In Texas Law Review 86, 2008, pp. 1409–1444
  48. Noam Kolt “Algorithmic Black Swans” In Washington University Law Review 101 URL: https://papers.ssrn.com/abstract=4370566
  49. Noam Kolt “Governing AI Agents”, 2024 URL: https://papers.ssrn.com/abstract=4772956
  50. Nathan Lambert, Thomas Krendl Gilbert and Tom Zick “The History and Risks of Reinforcement Learning and Human Feedback” arXiv, 2023 DOI: 10.48550/arXiv.2310.13595
  51. “A Safe Harbor for AI Evaluation and Red Teaming” arXiv, 2024 DOI: 10.48550/arXiv.2403.04893
  52. Peter Madsen, Robin L. Dillon and Catherine H. Tinsley “Airline Safety Improvement Through Experience with Near-Misses: A Cautionary Tale” In Risk Analysis 36.5, 2015, pp. 1054–1066 DOI: 10.1111/risa.12503
  53. David Manheim “Building a Culture of Safety for AI: Perspectives and Challenges”, 2023 DOI: 10.2139/ssrn.4491421
  54. Gary E. Marchant and Carlos Ignacio Gutierrez “Soft Law 2.0: An Agile and Effective Governance Approach for Artificial Intelligence” In Minnesota Journal of Law, Science and Technology 24, 2022, pp. 375 URL: https://heinonline.org/HOL/Page?handle=hein.journals/mipr24&id=379&div=&collection=
  55. Sean McGregor “Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database” Number: 17 In Proceedings of the AAAI Conference on Artificial Intelligence 35.17, 2021, pp. 15458–15463 DOI: 10.1609/aaai.v35i17.17817
  56. Sean McGregor, Kevin Paeth and Khoa Lam “Indexing AI Risks with Incidents, Issues, and Variants” arXiv, 2022 DOI: 10.48550/arXiv.2211.10384
  57. “Model Cards for Model Reporting” In Proceedings of the Conference on Fairness, Accountability, and Transparency Atlanta GA USA: ACM, 2019, pp. 220–229 DOI: 10.1145/3287560.3287596
  58. “Responsible Scaling Policies (RSPs)”, 2023 URL: https://metr.org/blog/2023-09-26-rsp/
  59. “Auditing large language models: a three-layered approach” In AI and Ethics, 2023 DOI: 10.1007/s43681-023-00289-2
  60. National Institute of Standards and Technology “AI Risk Management Framework” In NIST, 2021 URL: https://www.nist.gov/itl/ai-risk-management-framework
  61. “Disclosure by Design: Designing information disclosures to support meaningful transparency and accountability” In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’22 New York, NY, USA: Association for Computing Machinery, 2022, pp. 679–690 DOI: 10.1145/3531146.3533133
  62. OECD.AI Network of Experts “Expert Group on AI Incidents” URL: https://oecd.ai/en//network-of-experts/working-group/10836
  63. “Towards AI Accountability Infrastructure: Gaps and Opportunities in AI Audit Tooling” arXiv, 2024 DOI: 10.48550/arXiv.2402.17861
  64. OpenAI “GPT-4 System Card”, 2023 URL: https://cdn.openai.com/papers/gpt-4-system-card.pdf
  65. OpenAI “GPT-4V(ision) system card”, 2023 URL: https://openai.com/research/gpt-4v-system-card
  66. OpenAI “Preparedness”, 2023 URL: https://cdn.openai.com/openai-preparedness-framework-beta.pdf
  67. “Evaluating Frontier Models for Dangerous Capabilities” arXiv, 2024 DOI: 10.48550/arXiv.2403.13793
  68. “Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance” In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’22 New York, NY, USA: Association for Computing Machinery, 2022, pp. 557–571 DOI: 10.1145/3514094.3534181
  69. “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context” arXiv, 2024 DOI: 10.48550/arXiv.2403.05530
  70. “Computing Power and the Governance of Artificial Intelligence” arXiv, 2024 DOI: 10.48550/arXiv.2402.08797
  71. “Towards best practices in AGI safety and governance: A survey of expert opinion” arXiv, 2023 DOI: 10.48550/arXiv.2305.07153
  72. Secretary of State for Science, Innovation and Technology “Policy paper: Introducing the AI Safety Institute” ISBN: 978-1-5286-4538-6, 2023 URL: https://www.gov.uk/government/publications/ai-safety-institute-overview/introducing-the-ai-safety-institute
  73. “Model evaluation for extreme risks” arXiv, 2023 DOI: 10.48550/arXiv.2305.15324
  74. Kris Shrishak “How to deal with an AI near-miss: Look to the skies” In Bulletin of the Atomic Scientists 79.3, 2023, pp. 166–169 DOI: 10.1080/00963402.2023.2199580
  75. Matthew C. Stephenson “Information Acquisition and Institutional Design” In Harvard Law Review 124.6, 2011, pp. 1422–1483 URL: https://harvardlawreview.org/print/vol-124/information-acquisition-and-institutional-design/
  76. The White House “Biden-Harris Administration Launches Artificial Intelligence Cyber Challenge to Protect America’s Critical Software”, 2023 URL: https://www.whitehouse.gov/briefing-room/statements-releases/2023/08/09/biden-harris-administration-launches-artificial-intelligence-cyber-challenge-to-protect-americas-critical-software/
  77. The White House “Blueprint for an AI Bill of Rights | OSTP”, 2022 URL: https://www.whitehouse.gov/ostp/ai-bill-of-rights/
  78. The White House “Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence”, 2023 URL: https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/
  79. The White House “FACT SHEET: Biden-Harris Administration Announces New Actions to Promote Responsible AI Innovation that Protects Americans’ Rights and Safety”, 2023 URL: https://www.whitehouse.gov/briefing-room/statements-releases/2023/05/04/fact-sheet-biden-harris-administration-announces-new-actions-to-promote-responsible-ai-innovation-that-protects-americans-rights-and-safety/
  80. The White House “FACT SHEET: Biden-Harris Administration Secures Voluntary Commitments from Leading Artificial Intelligence Companies to Manage the Risks Posed by AI”, 2023 URL: https://www.whitehouse.gov/briefing-room/statements-releases/2023/07/21/fact-sheet-biden-harris-administration-secures-voluntary-commitments-from-leading-artificial-intelligence-companies-to-manage-the-risks-posed-by-ai/
  81. “Skating to Where the Puck Is Going”, 2023 URL: https://cset.georgetown.edu/publication/skating-to-where-the-puck-is-going/
  82. “Llama 2: Open Foundation and Fine-Tuned Chat Models” arXiv, 2023 DOI: 10.48550/arXiv.2307.09288
  83. “Why We Need to Know More: Exploring the State of AI Incident Documentation Practices” In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, AIES ’23 New York, NY, USA: Association for Computing Machinery, 2023, pp. 576–583 DOI: 10.1145/3600211.3604700
  84. U.S. Department of Commerce “At the Direction of President Biden, Department of Commerce to Establish U.S. Artificial Intelligence Safety Institute to Lead Efforts on AI Safety”, 2023 URL: https://www.commerce.gov/news/press-releases/2023/11/direction-president-biden-department-commerce-establish-us-artificial
  85. U.S. Department of Commerce “Biden-Harris Administration Announces First-Ever Consortium Dedicated to AI Safety”, 2024 URL: https://www.commerce.gov/news/press-releases/2024/02/biden-harris-administration-announces-first-ever-consortium-dedicated
  86. Rory Van Loo “Regulatory Monitors: Policing Firms in the Compliance Era” In Columbia Law Review 119, 2019 URL: https://columbialawreview.org/content/regulatory-monitors-policing-firms-in-the-compliance-era/
  87. “Emergent Abilities of Large Language Models” arXiv, 2022 DOI: 10.48550/arXiv.2206.07682
  88. “Sociotechnical Safety Evaluation of Generative AI Systems” arXiv, 2023 DOI: 10.48550/arXiv.2310.11986
  89. “Why and How Governments Should Monitor AI Development” arXiv, 2021 DOI: 10.48550/arXiv.2108.12427
  90. Thomas Woodside “Keeping Up with the Frontier: Why Congress Should Codify Reporting Requirements For Advanced AI Systems”, 2024 URL: https://cset.georgetown.edu/article/keeping-up-with-the-frontier/
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Authors (10)
  1. Noam Kolt (12 papers)
  2. Markus Anderljung (29 papers)
  3. Joslyn Barnhart (4 papers)
  4. Asher Brass (1 paper)
  5. Kevin Esvelt (1 paper)
  6. Gillian K. Hadfield (10 papers)
  7. Lennart Heim (21 papers)
  8. Mikel Rodriguez (9 papers)
  9. Jonas B. Sandbrink (2 papers)
  10. Thomas Woodside (5 papers)
Citations (8)
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