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Comment published on 22 July 2024, see Mach. Learn. Knowl. Extr. 2024, 6(3), 1667-1669.
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Article

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

by
Ekaterina Novozhilova
1,*,
Kate Mays
2,
Sejin Paik
1 and
James E. Katz
1
1
College of Communications, Boston University, Boston, MA 02215, USA
2
Department of Community Development and Applied Economics, College of Agriculture and Life Sciences, University of Vermont, Burlington, VT 05405, USA
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2024, 6(1), 342-366; https://doi.org/10.3390/make6010017
Submission received: 8 December 2023 / Revised: 19 January 2024 / Accepted: 1 February 2024 / Published: 5 February 2024
(This article belongs to the Special Issue Fairness and Explanation for Trustworthy AI)
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Abstract

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.
Keywords: artificial intelligence; trust; survey; generative AI; AI ethics; AI governance artificial intelligence; trust; survey; generative AI; AI ethics; AI governance

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MDPI and ACS Style

Novozhilova, E.; Mays, K.; Paik, S.; Katz, J.E. More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts. Mach. Learn. Knowl. Extr. 2024, 6, 342-366. https://doi.org/10.3390/make6010017

AMA Style

Novozhilova E, Mays K, Paik S, Katz JE. More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts. Machine Learning and Knowledge Extraction. 2024; 6(1):342-366. https://doi.org/10.3390/make6010017

Chicago/Turabian Style

Novozhilova, Ekaterina, Kate Mays, Sejin Paik, and James E. Katz. 2024. "More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts" Machine Learning and Knowledge Extraction 6, no. 1: 342-366. https://doi.org/10.3390/make6010017

APA Style

Novozhilova, E., Mays, K., Paik, S., & Katz, J. E. (2024). More Capable, Less Benevolent: Trust Perceptions of AI Systems across Societal Contexts. Machine Learning and Knowledge Extraction, 6(1), 342-366. https://doi.org/10.3390/make6010017

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