The Role of Cooperation in Responsible AI Development

By Amanda Askell, Miles Brundage, Gillian K. Hadfield. 2019

74 citations recorded by the source corpus.

In this paper, we argue that competitive pressures could incentivize AI companies to underinvest in ensuring their systems are safe, secure, and have a positive social impact. Ensuring that AI systems are developed responsibly may therefore require preventing and solving collective action problems between companies. We note that there are several key factors that improve the prospects for cooperation in collective action problems. We use this to identify strategies to improve the prospects for industry cooperation on the responsible development of AI.

Subfields: Computer Science Applications, Safety Research, Sociology and Political Science

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Citations in the corpus

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020, background): “Greater verifiability of claims about AI development would help enable more effective oversight and reduce pressure to cut corners for the sake of gaining a competitive edge [1].”
Trustworthy AI: From Principles to Practices (2021, methodology): “Cooperation with regulators certifies the products to appropriately follow principles of trustworthiness.”
Augmenting large language models with chemistry tools (2023, background): “One major challenge is the lack of reproducibility of individual results under the current API-based approach to LLMs, as closed-source models provide limited control, see Appendix E. Recent open-source models 76–78 offer a potential solution to this issue, albeit with a possible trade-off in…”