TRUSTWORTHY AI REGULATION AND PUBLIC ACCEPTANCE: EVIDENCE FROM CITIZEN DELIBERATION PANELS

Abstract

The rapid integration of artificial intelligence into public services, healthcare, finance, education, policing, and digital governance has created growing concern about how AI systems can be regulated in ways that promote public trust and social acceptance. This study examines the relationship between trustworthy AI regulation and public acceptance using evidence from citizen deliberation panels. The research focuses on how public attitudes shift when citizens are provided with structured information, regulatory scenarios, and opportunities to deliberate on AI-related risks, benefits, and accountability mechanisms. The findings indicate that public acceptance of AI increases when regulatory frameworks emphasize transparency, human oversight, fairness, data protection, explainability, and institutional accountability. Participants expressed stronger support for AI deployment when systems were subject to independent audits, clear responsibility mechanisms, and meaningful channels for public participation. The results also show that concerns about privacy, algorithmic bias, surveillance, and loss of human control remain major barriers to acceptance. However, deliberation helped participants develop more balanced views by distinguishing between beneficial AI applications and high-risk uses requiring strict regulation. Overall, the study demonstrates that trustworthy AI governance is not only a technical or legal requirement but also a democratic process that depends on citizen engagement, institutional credibility, and visible safeguards. The paper contributes to debates on AI ethics, public policy, and participatory governance by showing that citizen deliberation can strengthen the legitimacy of AI regulation and improve public confidence in responsible AI adoption.