This paper is in the following e-collection/theme issue:

Quality/Credibility of eHealth Information and Trust Issues (654) Chatbots and Conversational Agents (740) Artificial Intelligence (2129) mHealth for Patient Education (292) User Needs and Competencies (456) Information Seeking, Information Needs (561)

Published on in Vol 23, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29386, first published .
The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

The Impact of Explanations on Layperson Trust in Artificial Intelligence–Driven Symptom Checker Apps: Experimental Study

Authors of this article:

Claire Woodcock1 Author Orcid Image ;   Brent Mittelstadt1 Author Orcid Image ;   Dan Busbridge Author Orcid Image ;   Grant Blank1 Author Orcid Image
Article Authors Cited by (30) Tweetations (49) Metrics

Journals

  1. Funnell E, Spadaro B, Benacek J, Martin-Key N, Metcalfe T, Olmert T, Barton-Owen G, Bahn S. Learnings from user feedback of a novel digital mental health assessment. Frontiers in Psychiatry 2022;13 View
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  14. Kim T, Im I. Understanding users’ AI manipulation intention: An empirical investigation of the antecedents in the context of AI recommendation algorithms. Information & Management 2025;62(1):104061 View
  15. Cheung J, Ho S. Explainable AI and trust: How news media shapes public support for AI-powered autonomous passenger drones. Public Understanding of Science 2025;34(3):344 View
  16. Lammert J, Roberts A, McRae K, Batterink L, Butler B. Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches. Journal of Speech, Language, and Hearing Research 2025;68(2):705 View
  17. Chow C, Zhang H, Cheng C. Cross-Lagged Analysis of Gender Differences in the Motivation-Cognition-Behavior Model of Gaming Disorder in Primary School Students. International Journal of Mental Health and Addiction 2025 View
  18. Xie Z. Assessing the impact of chatbots on health decision-making: A multifactorial experimental approach. Technology and Health Care 2025;33(5):2266 View
  19. Gibbard K, Gill H, Powell D, Hausdorf P. Explain it to me like I’m five: harnessing the power of explanations to increase trust in workplace generative AI. Behaviour & Information Technology 2025:1 View
  20. Rezaeian O, Asan O, Bayrak A. The impact of AI explanations on clinicians’ trust and diagnostic accuracy in breast cancer. Applied Ergonomics 2025;129:104577 View
  21. Kotzian P, Bauch K, Weißenberger B. Accountability and managerial advice-taking: comparing human and algorithmic advisers. Management Decision 2025;63(13):545 View
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Books/Policy Documents

  1. Rezaeian O, Bayrak A, Asan O. HCI International 2024 Posters. View

Conference Proceedings

  1. Bertrand A, Belloum R, Eagan J, Maxwell W. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society. How Cognitive Biases Affect XAI-assisted Decision-making View
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  4. Maehigashi A, Fukuchi Y, Yamada S. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. Adjusting Amount of AI Explanation for Visual Tasks View
  5. Lünich M, Keller B. The 2024 ACM Conference on Fairness, Accountability, and Transparency. Explainable Artificial Intelligence for Academic Performance Prediction. An Experimental Study on the Impact of Accuracy and Simplicity of Decision Trees on Causability and Fairness Perceptions View
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