mlsanityData to 14 Oct 2026

An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI

arXiv 2310.14455 ↗First posted 22 Oct 2023cs.CY cs.AIcited by 5

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Abstract (arXiv)

Given rapid progress toward advanced AI and risks from frontier AI systems (advanced AI systems pushing the boundaries of the AI capabilities frontier), the creation and implementation of AI governance and regulatory schemes deserves prioritization and substantial investment. However, the status quo is untenable and, frankly, dangerous. A regulatory gap has permitted AI labs to conduct research, development, and deployment activities with minimal oversight. In response, frontier AI system evaluations have been proposed as a way of assessing risks from the development and deployment of frontier AI systems. Yet, the budding AI risk evaluation ecosystem faces significant coordination challenges, such as a limited diversity of evaluators, suboptimal allocation of effort, and perverse incentives. This paper proposes a solution in the form of an international consortium for AI risk evaluations, comprising both AI developers and third-party AI risk evaluators. Such a consortium could play a critical role in international efforts to mitigate societal-scale risks from advanced AI, including in managing responsible scaling policies and coordinated evaluation-based risk response. In this paper, we discuss the current evaluation ecosystem and its shortcomings, propose an international consortium for advanced AI risk evaluations, discuss issues regarding its implementation, discuss lessons that can be learnt from previous international institutions and existing proposals for international AI governance institutions, and, finally, we recommend concrete steps to advance the establishment of the proposed consortium: (i) solicit feedback from stakeholders, (ii) conduct additional research, (iii) conduct a workshop(s) for stakeholders, (iv) analyze feedback and create final proposal, (v) solicit funding, and (vi) create a consortium.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 35 of its 116 references that are in the map. First posted on arXiv 22 Oct 2023.

Builds on

35 of 116 references.

References · 35

  1. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  2. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  5. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  6. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,729
  7. 2020Recipes for building an open-domain chatbotcited by 593
  8. 2020Towards a Human-like Open-Domain Chatbotcited by 470
  9. 2023Catastrophic Jailbreak of Open-source LLMs via Exploiting Generationcited by 451
  10. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  11. 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 258
  12. 2023An Overview of Catastrophic AI Riskscited by 222
23 more
  1. 2022Predictability and Surprise in Large Generative Modelscited by 210
  2. 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 165
  3. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
  4. 2018Categorizing Variants of Goodhart's Lawcited by 149
  5. 2023Auditing large language models: a three-layered approachcited by 137
  6. 2023Harms from Increasingly Agentic Algorithmic Systemscited by 135
  7. 2022The Fallacy of AI Functionalitycited by 88
  8. 2020On the Value of Out-of-Distribution Testing: An Example of Goodhart's Lawcited by 74
  9. 2022Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governancecited by 67
  10. 2023The Foundation Model Transparency Indexcited by 66
  11. 2023Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystemcited by 52
  12. 2022Structured access: an emerging paradigm for safe AI deploymentcited by 41
  13. 2023Towards best practices in AGI safety and governance: A survey of expert opinioncited by 27
  14. 2023TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AIcited by 26
  15. 2019The Role of Cooperation in Responsible AI Developmentcited by 21
  16. 2023What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoringcited by 21
  17. 2019The Transformative Potential of Artificial Intelligencecited by 15
  18. 2021AI Certification: Advancing Ethical Practice by Reducing Information Asymmetriescited by 15
  19. 2021Why and How Governments Should Monitor AI Developmentcited by 14
  20. 2019Regulatory Markets for AI Safetycited by 13
  21. 2023Coordinated pausing: An evaluation-based coordination scheme for frontier AI developerscited by 13
  22. 2023International Governance of Civilian AI: A Jurisdictional Certification Approachcited by 8
  23. 2023Auditing Gender Analyzers on Text Datacited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.