Humans incorrectly reject confident accusatory AI judgments

Bibliographic Data

ID21562009
AuthorsRiccardo Loconte (0000-0003-4688-1344, IMT School for Advanced Studies Lucca, corresponding author), Merylin Monaro (0000-0001-5598-691X, University of Padua), Pietro Pietrini (0000-0002-6768-5556, IMT School for Advanced Studies Lucca), Bruno Verschuere (0000-0002-6161-4415, University of Amsterdam), Benjamin Kleinberg (0000-0003-1658-9086, Crimean Agrotechnological University), Bennett Kleinberg
Year2026
Volume182
Pages109019
Publication date2026-09-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueComputers in Human Behavior (JOURNAL)
Journal identifiersISSN: 0747-5632 • E-ISSN: 1873-7692
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.chb.2026.109019
OpenAlexW4417010013
LanguageEN
References cited51

Automated verbal deception detection using methods from Artificial Intelligence (AI) has been shown to outperform humans in disentangling lies from truths. Research suggests that transparency and interpretability of computational methods tend to increase human acceptance of using AI to support decisions. However, the extent to which humans accept AI judgments for deception detection remains unclear. We experimentally examined how an AI model’s accuracy (i.e., its overall performance in deception detection) and confidence (i.e., the model’s uncertainty in single-statement predictions) influence human adoption of the model’s judgments. Participants ( n =373) were presented with veracity judgments of an AI model with high or low overall accuracy and various degrees of prediction confidence. The results showed that humans followed predictions from a highly accurate model more than from a less accurate one. Interestingly, the more confident the model, the more people deviated from it, especially if the model predicted deception. We also found that human interaction with algorithmic predictions either worsened the machine’s performance or was ineffective. While this human aversion to accept highly confident algorithmic predictions was partly explained by participants’ tendency to overestimate humans’ deception detection abilities, we also discuss how truth-default theory and the social costs of accusing someone of lying help explain the findings. • Accuracy of AI models increases human trust in model’s predictions • Humans reject highly confident AI predictions of deception • Human-AI interaction either worsens the machine’s performance or is ineffective

Deception · Interpretability · Lying · Overconfidence effect · Self-deception · Deception detection and forensic psychology · Explainable Artificial Intelligence (XAI · Psychology of Moral and Emotional Judgment

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