Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?
T.M. Peters, I. Scharlau, Frontiers in Psychology 16 (n.d.).
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Journal Article
| In Press
| English
Abstract
Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine,
trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception,
for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated
classification as advice. Research to improve such interactions often aims to foster the
user’s trust, which in turn should improve the combined human-AI performance. Given that AI
models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is
an equally interesting target of research.
We created two image classification scenarios in which the participants received mock-up
AI advice. The quality of the advice decreases for a phase of the experiment. We studied the
task performance, trust and distrust of the participants, and tested whether an instruction to
remain skeptical and review each piece of advice led to a better performance compared to a
neutral condition. Our results indicate that this instruction does not improve but rather worsens
the participants’ performance. Repeated single-item self-report of trust and distrust shows an
increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no
difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory
analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether
the problems of under- and over-reliance have been mitigated. We discuss implications of our
results for the usage of disclaimers before interacting with AI, as prominently used in current
LLM-based chatbots, and for trust and distrust research.
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Journal Title
Frontiers in Psychology
Volume
16
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Cite this
Peters TM, Scharlau I. Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications? Frontiers in Psychology. 16. doi:10.3389/fpsyg.2025.1574809
Peters, T. M., & Scharlau, I. (n.d.). Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications? Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1574809
@article{Peters_Scharlau, title={Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}, volume={16}, DOI={10.3389/fpsyg.2025.1574809}, journal={Frontiers in Psychology}, author={Peters, Tobias Martin and Scharlau, Ingrid} }
Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI: Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in Psychology 16 (n.d.). https://doi.org/10.3389/fpsyg.2025.1574809.
T. M. Peters and I. Scharlau, “Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?,” Frontiers in Psychology, vol. 16, doi: 10.3389/fpsyg.2025.1574809.
Peters, Tobias Martin, and Ingrid Scharlau. “Interacting with Fallible AI: Is Distrust Helpful When Receiving AI Misclassifications?” Frontiers in Psychology, vol. 16, doi:10.3389/fpsyg.2025.1574809.