Democratizing AI Governance: Balancing Expertise and Public Participation

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Autore principale: Ter-Minassian, Lucile
Natura: Preprint
Pubblicazione: 2025
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author Ter-Minassian, Lucile
author_facet Ter-Minassian, Lucile
contents The development and deployment of artificial intelligence (AI) systems, with their profound societal impacts, raise critical challenges for governance. Historically, technological innovations have been governed by concentrated expertise with limited public input. However, AI's pervasive influence across domains such as healthcare, employment, and justice necessitates inclusive governance approaches. This article explores the tension between expert-led oversight and democratic participation, analyzing models of participatory and deliberative democracy. Using case studies from France and Brazil, we highlight how inclusive frameworks can bridge the gap between technical complexity and public accountability. Recommendations are provided for integrating these approaches into a balanced governance model tailored to the European Union, emphasizing transparency, diversity, and adaptive regulation to ensure that AI governance reflects societal values while maintaining technical rigor. This analysis underscores the importance of hybrid frameworks that unite expertise and public voice in shaping the future of AI policy.
format Preprint
id arxiv_https___arxiv_org_abs_2502_08651
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Democratizing AI Governance: Balancing Expertise and Public Participation
Ter-Minassian, Lucile
Computers and Society
Machine Learning
The development and deployment of artificial intelligence (AI) systems, with their profound societal impacts, raise critical challenges for governance. Historically, technological innovations have been governed by concentrated expertise with limited public input. However, AI's pervasive influence across domains such as healthcare, employment, and justice necessitates inclusive governance approaches. This article explores the tension between expert-led oversight and democratic participation, analyzing models of participatory and deliberative democracy. Using case studies from France and Brazil, we highlight how inclusive frameworks can bridge the gap between technical complexity and public accountability. Recommendations are provided for integrating these approaches into a balanced governance model tailored to the European Union, emphasizing transparency, diversity, and adaptive regulation to ensure that AI governance reflects societal values while maintaining technical rigor. This analysis underscores the importance of hybrid frameworks that unite expertise and public voice in shaping the future of AI policy.
title Democratizing AI Governance: Balancing Expertise and Public Participation
topic Computers and Society
Machine Learning
url https://arxiv.org/abs/2502.08651