Voluntary safety commitments provide an escape from over-regulation in AI development
Not on the map. Why?
Abstract (arXiv)
With the introduction of Artificial Intelligence (AI) and related technologies in our daily lives, fear and anxiety about their misuse as well as the hidden biases in their creation have led to a demand for regulation to address such issues. Yet blindly 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. In this paper, starting from a baseline model that captures the fundamental dynamics of a race for domain supremacy using AI technology, we demonstrate how socially unwanted outcomes may be produced when sanctioning is applied unconditionally to risk-taking, i.e. potentially unsafe, behaviours. As an alternative to resolve the detrimental effect of over-regulation, we propose a voluntary commitment approach wherein technologists have the freedom of choice between independently pursuing their course of actions or establishing binding agreements to act safely, with sanctioning of those that do not abide to what they pledged. Overall, this work reveals for the first time how voluntary commitments, with sanctions either by peers or an institution, leads to socially beneficial outcomes in all scenarios envisageable in a short-term race towards domain supremacy through AI technology. These results are directly relevant for the design of governance and regulatory policies that aim to ensure an ethical and responsible AI technology development process.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 9 of its 61 references that are in the map. First posted on arXiv 8 Apr 2021.
Builds on
9 of 61 references.
References · 9
- 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
- 2019The Role of Cooperation in Responsible AI Developmentcited by 21
- 2019The Windfall Clause: Distributing the Benefits of AI for the Common Goodcited by 13
- 2020Mediating Artificial Intelligence Developments through Negative and Positive Incentivescited by 9
- 2021Cost efficiency of institutional incentives in finite populationscited by 8
- 2019Exploring optimal institutional incentives for public cooperationcited by 6
- 2020Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarizationcited by 4
- 2020Evolution of Coordination in Pairwise and Multi-player Interactions via Prior Commitmentscited by 4
- 2021Bridging the Gap: the case for an Incompletely Theorized Agreement on AI policycited by 3
Built on by
Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 2; 2026: 3.
10 papers.
- 2025Media and responsible AI governance: a game-theoretic and LLM analysiscited by 11
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulationcited by 9
- 2026Social physics in the age of artificial intelligencecited by 4
- 2024Evolutionary mechanisms that promote cooperation may not promote social welfarecited by 3
- 2025Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysiscited by 3
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safetycited by 2
- 2022Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theorycited by 1
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluationscited by 1
- 2026Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systemscited by 1
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experimentcited by 0
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment28 Jul 2026
- 2026Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems25 Mar 2026
- 2026Social physics in the age of artificial intelligence4 Mar 2026
- 2025Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysis2 Sep 2025
- 2025Media and responsible AI governance: a game-theoretic and LLM analysis12 Mar 2025
- 2024Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations19 Dec 2024
- 2024Evolutionary mechanisms that promote cooperation may not promote social welfare9 Aug 2024
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation14 Mar 2024
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety6 Mar 2023
- 2022Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theory15 May 2022