Trust in Artificial Intelligence: Meta-Analytic Findings

Kaplan, Alexandra D.; Kessler, Theresa T.; Brill, J. Christopher; Hancock, P. A.

doi:10.1177/00187208211013988

Trust in Artificial Intelligence: Meta-Analytic Findings

Journal Article · Fri May 28 04:00:00 EDT 2021 · Human Factors

DOI:https://doi.org/10.1177/00187208211013988· OSTI ID:2425834

^[1]; Kessler, Theresa T. ^[2]; Brill, J. Christopher ^[3]; ^[1]

University of Central Florida, Orlando, Florida, USA
Georgia Tech Research Institute, Atlanta, Georgia, USA
Air Force Research Laboratory, Dayton, Ohio, USA

Objective

The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction.

Background

There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI.

Method

Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors.

Results

Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others.

Conclusion

Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research.

Application

Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Research Organization:: Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: SC0014664

OSTI ID:: 2425834

Journal Information:: Human Factors, Journal Name: Human Factors Journal Issue: 2 Vol. 65; ISSN 0018-7208

Publisher:: SAGE

Country of Publication:: United States

Language:: English

References (14)

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Can You Trust Your Robot? Hancock, P. A.; Billings, D. R.; Schaefer, K. E. Ergonomics in Design: The Quarterly of Human Factors Applications, Vol. 19, Issue 3 https://doi.org/10.1177/1064804611415045	journal	July 2011
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