# Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysis

arXiv: https://arxiv.org/abs/2509.02650 · HTML: https://mlsanity.com/paper/2509.02650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 49 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [International AI Safety Report](https://mlsanity.com/paper/2501.17805/) (2025)
- [Unveiling Security, Privacy, and Ethical Concerns of ChatGPT](https://mlsanity.com/paper/2307.14192/) (2023)
- [Statistical physics of human cooperation](https://mlsanity.com/paper/1705.07161/) (2017)
- [Media and responsible AI governance: a game-theoretic and LLM analysis](https://mlsanity.com/paper/2503.09858/) (2025)
- [Self-Organization and Artificial Life](https://mlsanity.com/paper/1903.07456/) (2019)
- [Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents](https://mlsanity.com/paper/2504.08640/) (2025)
- [Voluntary safety commitments provide an escape from over-regulation in AI development](https://mlsanity.com/paper/2104.03741/) (2021)
- [Artificial Intelligence Development Races in Heterogeneous Settings](https://mlsanity.com/paper/2012.15234/) (2020)
- [Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation](https://mlsanity.com/paper/2403.09510/) (2024)

## Built on by (most cited first, 3 of 3)

- [Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics](https://mlsanity.com/paper/2512.07462/) (2025)
- [Social physics in the age of artificial intelligence](https://mlsanity.com/paper/2603.16900/) (2026)
- [Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems](https://mlsanity.com/paper/2603.24742/) (2026)

## Abstract (arXiv)

When developers of artificial intelligence (AI) products need to decide between profit and safety for the users, they likely choose profit. Untrustworthy AI technology must come packaged with tangible negative consequences. Here, we envisage those consequences as the loss of reputation caused by media coverage of their misdeeds, disseminated to the public. We explore whether media coverage has the potential to push AI creators into the production of safe products, enabling widespread adoption of AI technology. We created artificial populations of self-interested creators and users and studied them through the lens of evolutionary game theory. Our results reveal that media is indeed able to foster cooperation between creators and users, but not always. Cooperation does not evolve if the quality of the information provided by the media is not reliable enough, or if the costs of either accessing media or ensuring safety are too high. By shaping public perception and holding developers accountable, media emerges as a powerful soft regulator -- guiding AI safety even in the absence of formal government oversight.