Why and How Governments Should Monitor AI Development
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Abstract (arXiv)
In this paper we outline a proposal for improving the governance of artificial intelligence (AI) by investing in government capacity to systematically measure and monitor the capabilities and impacts of AI systems. If adopted, this would give governments greater information about the AI ecosystem, equipping them to more effectively direct AI development and deployment in the most societally and economically beneficial directions. It would also create infrastructure that could rapidly identify potential threats or harms that could occur as a consequence of changes in the AI ecosystem, such as the emergence of strategically transformative capabilities, or the deployment of harmful systems. We begin by outlining the problem which motivates this proposal: in brief, traditional governance approaches struggle to keep pace with the speed of progress in AI. We then present our proposal for addressing this problem: governments must invest in measurement and monitoring infrastructure. We discuss this proposal in detail, outlining what specific things governments could focus on measuring and monitoring, and the kinds of benefits this would generate for policymaking. Finally, we outline some potential pilot projects and some considerations for implementing this in practice.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 28 Aug 2021.
Builds on
7 of 23 references.
References · 7
- 2020Scaling Laws for Neural Language Modelscited by 6,411
- 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
- 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
- 2020Scaling Laws for Autoregressive Generative Modelingcited by 510
- 2018Gender Bias in Coreference Resolutioncited by 438
- 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 159
- 2021The AI Index 2021 Annual Reportcited by 34
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Citing papers per year: 2022: 2; 2023: 6; 2024: 3; 2025: 2.
13 papers.
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- 2023Model evaluation for extreme riskscited by 167
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
- 2023Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Frameworkcited by 11
- 2024Responsible Reporting for Frontier AI Developmentcited by 10
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulationcited by 9
- 2023An International Consortium for Evaluations of Societal-Scale Risks from Advanced AIcited by 5
- 2022Negative Human Rights as a Basis for Long-term AI Safety and Regulationcited by 3
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safetycited by 2
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluationscited by 1
- 2025One Bad NOFO? AI Governance in Federal Grantmakingcited by 1
- 2025Risks of AI-driven product development and strategies for their mitigationcited by 1
- 2022Artificial Intelligence and Arms Controlcited by 0
- 2025Risks of AI-driven product development and strategies for their mitigation28 May 2025
- 2025One Bad NOFO? AI Governance in Federal Grantmaking13 May 2025
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations19 Dec 2024
- 2024Responsible Reporting for Frontier AI Development3 Apr 2024
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation14 Mar 2024
- 2023Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework15 Nov 2023
- 2023An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI22 Oct 2023
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safety6 Jul 2023
- 2023Model evaluation for extreme risks24 May 2023
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders22 Mar 2023
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety6 Mar 2023
- 2022Artificial Intelligence and Arms Control22 Oct 2022
1 more
- 2023Model evaluation for extreme riskscited by 167
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safetycited by 95
- 2023Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Frameworkcited by 11
- 2024Responsible Reporting for Frontier AI Developmentcited by 10
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulationcited by 9
- 2023An International Consortium for Evaluations of Societal-Scale Risks from Advanced AIcited by 5
- 2022Negative Human Rights as a Basis for Long-term AI Safety and Regulationcited by 3
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safetycited by 2
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluationscited by 1
- 2025One Bad NOFO? AI Governance in Federal Grantmakingcited by 1
- 2025Risks of AI-driven product development and strategies for their mitigationcited by 1
- 2022Artificial Intelligence and Arms Controlcited by 0
12 most cited of 13 All 13 (JSON)
- 2025Risks of AI-driven product development and strategies for their mitigation28 May 2025
- 2025One Bad NOFO? AI Governance in Federal Grantmaking13 May 2025
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations19 Dec 2024
- 2024Responsible Reporting for Frontier AI Development3 Apr 2024
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation14 Mar 2024
- 2023Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework15 Nov 2023
- 2023An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI22 Oct 2023
- 2023Frontier AI Regulation: Managing Emerging Risks to Public Safety6 Jul 2023
- 2023Model evaluation for extreme risks24 May 2023
- 2023Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders22 Mar 2023
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety6 Mar 2023
- 2022Artificial Intelligence and Arms Control22 Oct 2022
12 newest of 13 All 13 (JSON)