Explainability increases trust resilience in intelligent agents.
Even though artificial intelligence (AI)‐based systems typically outperform human decision‐makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again—a phenomenon known as algorithm aversion. The purpose of the present research was to investigate...
| Published in: | British Journal of Psychology Vol. 117; no. 2; pp. 528 - 548 |
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| Main Authors: | , |
| Format: | Article |
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Wiley-Blackwell
May2026
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192785882&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192785882 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00071269 BJP jtl: British Journal of Psychology issn: 00071269 maglogo: Y pubinfo: dt: May2026 vid: 117 iid: 2 pid: 480 pub: Wiley-Blackwell artinfo: ui: 192785882 10.1111/bjop.12740 ppf: 528 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 1.3MB tig: atl: Explainability increases trust resilience in intelligent agents. aug: au: Xu, Min Wang, Yiwen affil: School of Economics and Management, Fuzhou University, Fuzhou, China su: China Crowdsourcing Task performance Satisfaction Artificial intelligence Undergraduates Universities & colleges Health Confidence Decision making Information resources Emotions Trust Analysis of variance Avoidance (Psychology) Forecasting Psychological resilience Repeated measures design T-test (Statistics) Data analysis Research funding Attitudes toward computers Statistical sampling Logistic regression analysis Experimental design Odds ratio Statistics Data analysis software Factor analysis Algorithms Evaluation sug: subj: Crowdsourcing Task performance Satisfaction Artificial intelligence Undergraduates Universities & colleges Health Confidence Decision making Information resources Emotions Trust Analysis of variance Avoidance (Psychology) Forecasting China Marketing Research and Public Opinion Polling Colleges, Universities, and Professional Schools Psychological resilience Repeated measures design T-test (Statistics) Data analysis Research funding Attitudes toward computers Statistical sampling Logistic regression analysis Experimental design Odds ratio Statistics Data analysis software Factor analysis Algorithms Evaluation keyword: algorithm aversion explainable artificial intelligence human‐AI interaction trust user experience algorithm aversion explainable artificial intelligence human‐AI interaction trust user experience ab: Even though artificial intelligence (AI)‐based systems typically outperform human decision‐makers, they are not immune to errors, leading users to lose trust in them and be less likely to use them again—a phenomenon known as algorithm aversion. The purpose of the present research was to investigate whether explainable AI (XAI) could function as a viable strategy to counter algorithm aversion. We conducted two experiments to examine how XAI influences users' willingness to continue using AI‐based systems when these systems exhibit errors. The results showed that, following the observation of algorithms erring, the inclination of users to delegate decisions to or follow advice from intelligent agents significantly decreased compared to the period before the errors were revealed. However, the explainability effectively mitigated this decline, with users in the XAI condition being more likely to continue utilizing intelligent agents for subsequent tasks after seeing algorithms erring than those in the non‐XAI condition. We further found that the explainability could reduce users' decision regret, and the decrease in decision regret mediated the relationship between the explainability and re‐use behaviour. These findings underscore the adaptive function of XAI in alleviating negative user experiences and maintaining user trust in the context of imperfect AI. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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