Media and responsible AI governance: a game-theoretic and LLM analysis
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2025
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| author | Balabanova, Nataliya Bashir, Adeela Bova, Paolo Buscemi, Alessio Cimpeanu, Theodor da Fonseca, Henrique Correia Di Stefano, Alessandro Duong, Manh Hong Domingos, Elias Fernandez Fernandes, Antonio Han, The Anh Krellner, Marcus Ogbo, Ndidi Bianca Powers, Simon T. Proverbio, Daniele Santos, Fernando P. Shamszaman, Zia Ush Song, Zhao |
| author_facet | Balabanova, Nataliya Bashir, Adeela Bova, Paolo Buscemi, Alessio Cimpeanu, Theodor da Fonseca, Henrique Correia Di Stefano, Alessandro Duong, Manh Hong Domingos, Elias Fernandez Fernandes, Antonio Han, The Anh Krellner, Marcus Ogbo, Ndidi Bianca Powers, Simon T. Proverbio, Daniele Santos, Fernando P. Shamszaman, Zia Ush Song, Zhao |
| contents | This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes. The research explores two key mechanisms for achieving responsible governance, safe AI development and adoption of safe AI: incentivising effective regulation through media reporting, and conditioning user trust on commentariats' recommendation. The findings highlight the crucial role of the media in providing information to users, potentially acting as a form of "soft" regulation by investigating developers or regulators, as a substitute to institutional AI regulation (which is still absent in many regions). Both game-theoretic analysis and LLM-based simulations reveal conditions under which effective regulation and trustworthy AI development emerge, emphasising the importance of considering the influence of different regulatory regimes from an evolutionary game-theoretic perspective. The study concludes that effective governance requires managing incentives and costs for high quality commentaries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Media and responsible AI governance: a game-theoretic and LLM analysis Balabanova, Nataliya Bashir, Adeela Bova, Paolo Buscemi, Alessio Cimpeanu, Theodor da Fonseca, Henrique Correia Di Stefano, Alessandro Duong, Manh Hong Domingos, Elias Fernandez Fernandes, Antonio Han, The Anh Krellner, Marcus Ogbo, Ndidi Bianca Powers, Simon T. Proverbio, Daniele Santos, Fernando P. Shamszaman, Zia Ush Song, Zhao Artificial Intelligence Computer Science and Game Theory Multiagent Systems Chaotic Dynamics This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes. The research explores two key mechanisms for achieving responsible governance, safe AI development and adoption of safe AI: incentivising effective regulation through media reporting, and conditioning user trust on commentariats' recommendation. The findings highlight the crucial role of the media in providing information to users, potentially acting as a form of "soft" regulation by investigating developers or regulators, as a substitute to institutional AI regulation (which is still absent in many regions). Both game-theoretic analysis and LLM-based simulations reveal conditions under which effective regulation and trustworthy AI development emerge, emphasising the importance of considering the influence of different regulatory regimes from an evolutionary game-theoretic perspective. The study concludes that effective governance requires managing incentives and costs for high quality commentaries. |
| title | Media and responsible AI governance: a game-theoretic and LLM analysis |
| topic | Artificial Intelligence Computer Science and Game Theory Multiagent Systems Chaotic Dynamics |
| url | https://arxiv.org/abs/2503.09858 |