# An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI arXiv: https://arxiv.org/abs/2310.14455 ยท HTML: https://mlsanity.com/paper/2310.14455/ ## 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 (most cited first, 30 of 35) - [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020) - [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018) - [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021) - [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022) - [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019) - [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020) - [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020) - [Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation](https://mlsanity.com/paper/2310.06987/) (2023) - [AI and the Everything in the Whole Wide World Benchmark](https://mlsanity.com/paper/2111.15366/) (2021) - [Risks from Learned Optimization in Advanced Machine Learning Systems](https://mlsanity.com/paper/1906.01820/) (2019) - [An Overview of Catastrophic AI Risks](https://mlsanity.com/paper/2306.12001/) (2023) - [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022) - [Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims](https://mlsanity.com/paper/2004.07213/) (2020) - [Sociotechnical Safety Evaluation of Generative AI Systems](https://mlsanity.com/paper/2310.11986/) (2023) - [Categorizing Variants of Goodhart's Law](https://mlsanity.com/paper/1803.04585/) (2018) - [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023) - [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023) - [The Fallacy of AI Functionality](https://mlsanity.com/paper/2206.09511/) (2022) - [On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law](https://mlsanity.com/paper/2005.09241/) (2020) - [Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance](https://mlsanity.com/paper/2206.04737/) (2022) - [The Foundation Model Transparency Index](https://mlsanity.com/paper/2310.12941/) (2023) - [Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem](https://mlsanity.com/paper/2310.02521/) (2023) - [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022) - [Towards best practices in AGI safety and governance: A survey of expert opinion](https://mlsanity.com/paper/2305.07153/) (2023) - [TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI](https://mlsanity.com/paper/2306.06924/) (2023) - [The Role of Cooperation in Responsible AI Development](https://mlsanity.com/paper/1907.04534/) (2019) - [What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring](https://mlsanity.com/paper/2303.11341/) (2023) - [The Transformative Potential of Artificial Intelligence](https://mlsanity.com/paper/1912.00747/) (2019) - [AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries](https://mlsanity.com/paper/2105.10356/) (2021) ## Built on by (most cited first, 5 of 5) - [Distributional AGI Safety](https://mlsanity.com/paper/2512.16856/) (2025) - [The Necessity of AI Audit Standards Boards](https://mlsanity.com/paper/2404.13060/) (2024) - [Ethical AI: Towards Defining a Collective Evaluation Framework](https://mlsanity.com/paper/2506.00233/) (2025) - [Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations](https://mlsanity.com/paper/2412.15433/) (2024) - [Precautionary Governance of Autonomous AI: Legal Personhood as Functional Instrument](https://mlsanity.com/paper/2605.12505/) (2026) ## 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.