Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this Perspective, we reflect on a governance initiative by one of the world’s largest AI conferences. In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. Drawing insights from similar governance initiatives, including institutional review boards (IRBs) and impact requirements for funding applications, we investigate the risks, challenges and potential benefits of such an initiative. Among the challenges, we list a lack of recognized best practice and procedural transparency, researcher opportunity costs, institutional and social pressures, cognitive biases and the inherently difficult nature of the task. The potential benefits, on the other hand, include improved anticipation and identification of impacts, better communication with policy and governance experts, and a general strengthening of the norms around responsible research. To maximize the chance of success, we recommend measures to increase transparency, improve guidance, create incentives to engage earnestly with the process, and facilitate public deliberation on the requirement’s merits and future. Perhaps the most important contribution from this analysis are the insights we can gain regarding effective community-based governance and the role and responsibility of the AI research community more broadly.
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We thank J. Tenenbaum, Y. Gal, T. Shevlane and colleagues at the Centre for the Governance of AI for helpful feedback and comments.
The authors declare no competing interests.
Peer review information Nature Machine Intelligence thanks Gillian Hadfield, Sean Legassick and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Prunkl, C.E.A., Ashurst, C., Anderljung, M. et al. Institutionalizing ethics in AI through broader impact requirements. Nat Mach Intell 3, 104–110 (2021). https://doi.org/10.1038/s42256-021-00298-y
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