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Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust

Authors:

Nikola Banovic,

Alice LiuAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 7, Issue CSCW1

Article No.: 27, Pages 1 - 17

https://doi.org/10.1145/3579460

Published: 16 April 2023 Publication History

Abstract

Trustworthy Artificial Intelligence (AI) is characterized, among other things, by: 1) competence, 2) transparency, and 3) fairness. However, end-users may fail to recognize incompetent AI, allowing untrustworthy AI to exaggerate its competence under the guise of transparency to gain unfair advantage over other trustworthy AI. Here, we conducted an experiment with 120 participants to test if untrustworthy AI can deceive end-users to gain their trust. Participants interacted with two AI-based chess engines, trustworthy (competent, fair) and untrustworthy (incompetent, unfair), that coached participants by suggesting chess moves in three games against another engine opponent. We varied coaches' transparency about their competence (with the untrustworthy one always exaggerating its competence). We quantified and objectively measured participants' trust based on how often participants relied on coaches' move recommendations. Participants showed inability to assess AI competence by misplacing their trust with the untrustworthy AI, confirming its ability to deceive. Our work calls for design of interactions to help end-users assess AI trustworthiness.

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Cited By

Festor PNagendran MGordon AFaisal AKomorowski M(2025)Safety of human-AI cooperative decision-making within intensive care: A physical simulation studyPLOS Digital Health10.1371/journal.pdig.00007264:2(e0000726)Online publication date: 24-Feb-2025
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Proceedings of the ACM on Human-Computer Interaction Volume 7, Issue CSCW1

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April 2023

3836 pages

EISSN:2573-0142

DOI:10.1145/3593053

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Published: 16 April 2023

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Cited By

Festor PNagendran MGordon AFaisal AKomorowski M(2025)Safety of human-AI cooperative decision-making within intensive care: A physical simulation studyPLOS Digital Health10.1371/journal.pdig.00007264:2(e0000726)Online publication date: 24-Feb-2025
https://doi.org/10.1371/journal.pdig.0000726
Cohen JHuan X(2024)Uncertainty-aware explainable AI as a foundational paradigm for digital twinsFrontiers in Mechanical Engineering10.3389/fmech.2023.13291469Online publication date: 5-Jan-2024
https://doi.org/10.3389/fmech.2023.1329146
Ferguson SAoyagui PRizvi RKim YKuzminykh A(2024)The Explanation That Hits Home: The Characteristics of Verbal Explanations That Affect Human Perception in Subjective Decision-MakingProceedings of the ACM on Human-Computer Interaction10.1145/36870568:CSCW2(1-37)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687056
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