mlsanityData to 7 Oct 2026

Why and How Governments Should Monitor AI Development

arXiv 2108.12427 ↗First posted 28 Aug 2021cs.CY cs.AIcited by 13

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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

  1. 2020Scaling Laws for Neural Language Modelscited by 6,411
  2. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  3. 2017Deep Learning Scaling is Predictable, Empiricallycited by 556
  4. 2020Scaling Laws for Autoregressive Generative Modelingcited by 510
  5. 2018Gender Bias in Coreference Resolutioncited by 438
  6. 2018The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigationcited by 159
  7. 2021The AI Index 2021 Annual Reportcited by 34

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Citing papers per year: 2022: 2; 2023: 6; 2024: 3; 2025: 2.

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