Perceptions of AI Across Sectors: A Comparative Review of Public Attitudes

Public trust in AI varies dramatically by sector: The review of 251 studies reveals that acceptance of AI is not uniform—people's willingness to adopt AI in healthcare differs significantly from attitudes toward autonomous vehicles or AI in security contexts, suggesting that sector-specific governance strategies are necessary rather than one-size-fits-all approaches.

Technical performance alone doesn't determine public acceptance: The paper finds that how well an AI system actually works is only part of the story; public perception is equally shaped by cultural narratives, historical context, and institutional trust, meaning that responsible AI requires attention to social and ethical factors beyond engineering improvements.

Fairness and institutional trust are critical concerns: Across domains, perceived fairness and trust in the institutions deploying AI emerge as recurring factors influencing public acceptance, indicating that organizations implementing AI systems must prioritize transparency and demonstrate commitment to equitable outcomes to gain public support.

Ethical concerns and risk perception vary by use-case: Different AI applications trigger different concerns—for example, security applications may raise privacy concerns while healthcare AI may raise questions about accountability—requiring tailored communication and governance strategies that address domain-specific ethical issues.

Cultural context and historical legacies shape AI imaginaries: The review demonstrates that public attitudes are influenced by existing cultural narratives and historical experiences with technology, suggesting that effective responsible AI governance must engage with local contexts and address pre-existing concerns rooted in community history.