Understanding support for AI regulation: A Bayesian network perspective

TL;DR

This paper uses Bayesian networks to analyze German public attitudes towards AI regulation, revealing how perceptions, political views, and information influence support for AI policies, aiding effective governance strategies.

Contribution

It introduces a data-driven Bayesian network approach to model public perceptions of AI regulation, integrating survey data and identifying key belief patterns and demographic influences.

Findings

Awareness of regulation is driven by information-seeking behavior.

Support for legal AI restrictions depends on perceived policy effectiveness.

Political orientation and age significantly influence regulatory attitudes.

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.