Understanding support for AI regulation: A Bayesian network perspective

• URL: http://arxiv.org/abs/2507.05866v1
• Date: Tue, 08 Jul 2025 10:47:10 GMT
• Title: Understanding support for AI regulation: A Bayesian network perspective
• Authors: Andrea Cremaschi, Dae-Jin Lee, Manuele Leonelli,
• Abstract summary: This study models public attitudes using Bayesian networks learned from the 2023 German survey Current Questions on AI.<n>The survey includes variables on AI interest, exposure, perceived threats and opportunities, awareness of EU regulation, and support for legal restrictions.<n>We show that awareness of regulation is driven by information-seeking behavior, while support for legal requirements depends strongly on perceived policy adequacy and political alignment.
• Score: 1.8434042562191815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As artificial intelligence (AI) becomes increasingly embedded in public and private life, understanding how citizens perceive its risks, benefits, and regulatory needs is essential. To inform ongoing regulatory efforts such as the European Union's proposed AI Act, this study models public attitudes using Bayesian networks learned from the nationally representative 2023 German survey Current Questions on AI. The survey includes variables on AI interest, exposure, perceived threats and opportunities, awareness of EU regulation, and support for legal restrictions, along with key demographic and political indicators. We estimate probabilistic models that reveal how personal engagement and techno-optimism shape public perceptions, and how political orientation and age influence regulatory attitudes. Sobol indices and conditional inference identify belief patterns and scenario-specific responses across population profiles. We show that awareness of regulation is driven by information-seeking behavior, while support for legal requirements depends strongly on perceived policy adequacy and political alignment. Our approach offers a transparent, data-driven framework for identifying which public segments are most responsive to AI policy initiatives, providing insights to inform risk communication and governance strategies. We illustrate this through a focused analysis of support for AI regulation, quantifying the influence of political ideology, perceived risks, and regulatory awareness under different scenarios.

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