Voluntary safety commitments provide an escape from over-regulation in AI development

The Anh Han, Tom Lenaerts, Francisco C. Santos and Luís Moniz Pereira

Technology in Society, 2022, vol. 68, issue C

Abstract: With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as their inherent biases, incorporated during their creation, have led to a demand for governance and associated regulation. Yet regulating an innovation process that is not well understood may stifle this process and reduce benefits that society may gain from the generated technology, even under the best intentions. Instruments to shed light on such processes are thus needed as they can ensure that imposed policies achieve the ambitions for which they were designed. Starting from a game-theoretical model that captures the fundamental dynamics of a race for domain supremacy using AI technology, we show how socially unwanted outcomes may be produced when sanctioning is applied unconditionally to risk-taking, i.e. potentially unsafe, behaviours. We demonstrate here the potential of a regulatory approach that combines a voluntary commitment approach reminiscent of soft law, wherein technologists have the freedom of choice between independently pursuing their course of actions or establishing binding agreements to act safely, with either a peer or governmental sanctioning system of those that do not abide by what they pledged. As commitments are binding and sanctioned, they go beyond the classic view of soft law, akin more closely to actual law-enforced regulation. Overall, this work reveals how voluntary but sanctionable commitments generate socially beneficial outcomes in all scenarios envisageable in a short-term race towards domain supremacy through AI technology. These results provide an original dynamic systems perspective of the governance potential of enforceable soft law techniques or co-regulatory mechanisms, showing how they may impact the ambitions of developers in the context of the AI-based applications.

Keywords: Evolutionary game theory; AI development Race; Commitments; Incentives; Safety (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:68:y:2022:i:c:s0160791x21003183

DOI: 10.1016/j.techsoc.2021.101843

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