Media and responsible AI governance: a game-theoretic and LLM analysis

2025-03-12

AI Summary

This study addresses strategic interactions among AI developers, regulators, users, and media under heterogeneous regulatory environments, focusing on how media can compensate for institutional regulatory gaps. Method: We integrate evolutionary game theory with large language models (LLMs) to construct a multi-agent dynamic simulation framework—introducing, for the first time, media as a “soft regulatory” mechanism. Through game-theoretic modeling, strategy evolution analysis, and policy scenario evaluation, we identify incentive conditions and cost thresholds for high-quality media commentary. Contribution/Results: Empirical findings demonstrate that effective media oversight significantly enhances user trust and developer compliance rates. The framework provides a viable alternative governance pathway for deploying trustworthy AI, offering actionable insights for policymakers and platform stakeholders seeking scalable, adaptive regulatory instruments beyond traditional top-down enforcement.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

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.

Problem

• Analyzes interplay between AI developers, regulators, users, and media.
• Explores media's role in incentivizing regulation and building user trust.
• Models strategic interactions using game theory and LLM simulations.

Innovation

• Uses evolutionary game theory for AI governance analysis
• Incorporates large language models for strategic interaction modeling
• Explores media's role as soft regulation in AI development