More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts

Ekaterina Novozhilova, Kate Mays, Sejin Paik, James E. Katz

Modern AI applications have caused broad societal implications across key public domains. While previous research primarily focuses on individual user perspectives regarding AI systems, this study expands our understanding to encompass general public perceptions. Through a survey (N = 1506), we examined public trust across various tasks within education, healthcare, and creative arts domains. The results show that participants vary in their trust across domains. Notably, AI systems’ abilities were evaluated higher than their benevolence across all domains. Demographic traits had less influence on trust in AI abilities and benevolence compared to technology-related factors. Specifically, participants with greater technological competence, AI familiarity, and knowledge viewed AI as more capable in all domains. These participants also perceived greater systems’ benevolence in healthcare and creative arts but not in education. We discuss the importance of considering public trust and its determinants in AI adoption.