Media and responsible AI governance: a game-theoretic and LLM analysis
- URL: http://arxiv.org/abs/2503.09858v1
- Date: Wed, 12 Mar 2025 21:39:38 GMT
- Title: Media and responsible AI governance: a game-theoretic and LLM analysis
- Authors: Nataliya Balabanova, Adeela Bashir, Paolo Bova, Alessio Buscemi, Theodor Cimpeanu, Henrique Correia da Fonseca, Alessandro Di Stefano, Manh Hong Duong, Elias Fernandez Domingos, Antonio Fernandes, The Anh Han, Marcus Krellner, Ndidi Bianca Ogbo, Simon T. Powers, Daniele Proverbio, Fernando P. Santos, Zia Ush Shamszaman, Zhao Song,
- Abstract summary: This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
- Score: 61.132523071109354
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents [61.132523071109354]
This paper investigates the interplay between AI developers, regulators and users, modelling their strategic choices under different regulatory scenarios.
Our research identifies emerging behaviours of strategic AI agents, which tend to adopt more "pessimistic" stances than pure game-theoretic agents.
arXiv Detail & Related papers (2025-04-11T15:41:21Z) - AI and the Transformation of Accountability and Discretion in Urban Governance [1.9152655229960793]
The study synthesizes insights to propose guiding principles for responsible AI integration in decision-making processes.
The analysis argues that AI does not simply restrict or enhance discretion but redistributes it across institutional levels.
It may simultaneously strengthen managerial oversight, enhance decision-making consistency, and improve operational efficiency.
arXiv Detail & Related papers (2025-02-18T18:11:39Z) - Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems.
Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
arXiv Detail & Related papers (2024-11-05T17:17:45Z) - Can We Trust AI Agents? An Experimental Study Towards Trustworthy LLM-Based Multi-Agent Systems for AI Ethics [10.084913433923566]
This study examines how trustworthiness-enhancing techniques affect ethical AI output generation.
We design the prototype LLM-BMAS, where agents engage in structured discussions on real-world ethical AI issues.
Discussions reveal terms like bias detection, transparency, accountability, user consent, compliance, fairness evaluation, and EU AI Act compliance.
arXiv Detail & Related papers (2024-10-25T20:17:59Z) - Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act)
Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence.
As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
arXiv Detail & Related papers (2024-10-10T17:38:38Z) - Generative AI Needs Adaptive Governance [0.0]
generative AI challenges the notions of governance, trust, and human agency.
This paper argues that generative AI calls for adaptive governance.
We outline actors, roles, as well as both shared and actors-specific policy activities.
arXiv Detail & Related papers (2024-06-06T23:47:14Z) - AI Governance and Accountability: An Analysis of Anthropic's Claude [0.0]
This paper examines the AI governance landscape, focusing on Anthropic's Claude, a foundational AI model.
We analyze Claude through the lens of the NIST AI Risk Management Framework and the EU AI Act, identifying potential threats and proposing mitigation strategies.
arXiv Detail & Related papers (2024-05-02T23:37:06Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Putting AI Ethics into Practice: The Hourglass Model of Organizational
AI Governance [0.0]
We present an AI governance framework, which targets organizations that develop and use AI systems.
The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice.
arXiv Detail & Related papers (2022-06-01T08:55:27Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.