Don’t Be Fooled: The Misinformation Effect of Explanations in Human–AI Collaboration
Open accessAbstract
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems’ reasoning. To counter this opacity, explainable AI (XAI) methods promise enhanced transparency and interpretability. While recent studies have explored how XAI affects human–AI collaboration, few have examined the potential pitfalls caused by incorrect explanations. The implications for humans can be far-reaching but have not been explored extensively. To investigate this, we conducted a study (n = 160) on AI-assisted decision-making in which humans were supported by XAI. Our findings reveal a misinformation effect when incorrect explanations accompany correct AI advice with implications post-collaboration. This effect causes humans to infer flawed reasoning strategies, hindering task execution and demonstrating impaired procedural knowledge. Additionally, incorrect explanations compromise human–AI team performance during collaboration. With our work, we contribute to HCI by providing empirical evidence for the negative consequences of incorrect explanations on humans post-collaboration and outlining guidelines for designers of AI.
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As noted by Spitzer et al., misleading XAI explanations can create a 'misinformation effect,' where users might draw incorrect conclusions about the reasoning behind AI predictions.
“As noted by Spitzer et al. [40], such misleading explanations can create a “misinformation effect,” where users might draw incorrect conclusions about the reasoning behind AI predictions.”
From Explainable AI for critical care: a systematic review of interpretable models for sepsis and ICU mortality prediction (2026) · cites this paper as [40]
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- Authors
- Spitzer, P., Holstein, J., Morrison, K., Holstein, K., Satzger, G., & Kühl, N.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Carnegie Mellon University (US)
- University of Bayreuth (DE)
- Corresponding author
- Spitzer, Philipp
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2025-11-14
- Volume
- 42
- Issue
- 12
- Pages
- 8996-9024
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4416226706
- Cited by
- 12
- References
- 86
- Citations by year
- 2026: 12
- FWCI
- 4.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Human-Computer Interaction (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- explainable AI, misinformation effect, human-AI collaboration, AI-assisted decision-making, black-box AI, procedural knowledge, trust in AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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