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

arXiv: https://arxiv.org/abs/2104.03741 · HTML: https://mlsanity.com/paper/2104.03741/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [The Role of Cooperation in Responsible AI Development](https://mlsanity.com/paper/1907.04534/) (2019)
- [The Windfall Clause: Distributing the Benefits of AI for the Common Good](https://mlsanity.com/paper/1912.11595/) (2019)
- [Mediating Artificial Intelligence Developments through Negative and Positive Incentives](https://mlsanity.com/paper/2010.00403/) (2020)
- [Cost efficiency of institutional incentives in finite populations](https://mlsanity.com/paper/2103.01131/) (2021)
- [Exploring optimal institutional incentives for public cooperation](https://mlsanity.com/paper/1907.10989/) (2019)
- [Timing uncertainty in collective risk dilemmas encourages group reciprocation and polarization](https://mlsanity.com/paper/2003.07317/) (2020)
- [Evolution of Coordination in Pairwise and Multi-player Interactions via Prior Commitments](https://mlsanity.com/paper/2009.11727/) (2020)
- [Bridging the Gap: the case for an Incompletely Theorized Agreement on AI policy](https://mlsanity.com/paper/2101.06110/) (2021)

## Built on by (most cited first, 10 of 10)

- [Media and responsible AI governance: a game-theoretic and LLM analysis](https://mlsanity.com/paper/2503.09858/) (2025)
- [Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation](https://mlsanity.com/paper/2403.09510/) (2024)
- [Social physics in the age of artificial intelligence](https://mlsanity.com/paper/2603.16900/) (2026)
- [Evolutionary mechanisms that promote cooperation may not promote social welfare](https://mlsanity.com/paper/2408.05373/) (2024)
- [Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysis](https://mlsanity.com/paper/2509.02650/) (2025)
- [Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety](https://mlsanity.com/paper/2303.03174/) (2023)
- [Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theory](https://mlsanity.com/paper/2205.07369/) (2022)
- [Quantifying detection rates for dangerous capabilities: a theoretical model of dangerous capability evaluations](https://mlsanity.com/paper/2412.15433/) (2024)
- [Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems](https://mlsanity.com/paper/2603.24742/) (2026)
- [Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment](https://mlsanity.com/paper/2607.26034/) (2026)

## 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.