Deceptive Explanations by Large Language Models Lead People to Change their Beliefs About Misinformation More Often than Honest Explanations
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Hallucinated content in substantive documents becomes incorporated into downstream outputs, possibly eroding the factual basis for high-stakes decisions.
“When it appears in substantive documents, hallucinated content then becomes incorporated into downstream outputs, possibly eroding the factual basis for high-stakes decisions [2].”
From LLM hallucinations in the wild: Large-scale evidence from non-existent citations (2026) · cites this paper as [2]
The issue of generative AI facilitating CSEA remains under-addressed in the literature.
“This issue remains under-addressed in the literature.”
From Generative AI in child sexual exploitation and abuse: views from UK law enforcement (2026) · cites this paper as Danry et al. 2025
AI safety work has largely focussed on domains such as dis/misinformation, fraud, hate speech, and online abuse, while CSEA remains comparatively underexplored.
“AI safety work has largely focussed on domains such as dis/misinformation, fraud, hate speech, and online abuse (e.g. Danry et al. 2025; Guo 2024; Lee et al. 2024), while CSEA remains comparatively underexplored.”
From Generative AI in child sexual exploitation and abuse: views from UK law enforcement (2026) · cites this paper as Danry et al. 2025
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Pataranutaporn, P., Lee, E., Amores, J., & Maes, P. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791851 · Cited by 1 · see citing works
Self-citationUnderstanding Compliance and Conversion Dynamics in Multi-Agent Collectives
Lee, S., & Lee, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790385
Determining the Educational Neuroscience Literacy of Generative AI
Saygıner, Ş. (2026) · Erzincan Üniversitesi Eğitim Fakültesi Dergisi · doi:10.17556/erziefd.1777914
Aktaş, İ. (2026) · Fen Bilimleri Öğretimi Dergisi · doi:10.56423/fbod.1788435
Pataranutaporn, P., Albrecht, C., Archiwaranguprok, C., Karny, S., & Baez, A. (2026) · Augmented Humans International Conference (AHs) · doi:10.1145/3795011.3795070
Self-citationAffective Explanations for Autonomous Vehicles: From Framework to Scenario-Based Design Guidelines
Jin, S., Zhu, Y., Chen, X., Liu, M. Q., & Wang, S. J. (2026) · Designing Interactive Systems Conference · doi:10.1145/3800645.3812927
Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers
Imteyaz, K., Hurtado, I. L., Rajpal, N., Shin, H., & Savage, S. (2026) · Annual Symposium on Human-Computer Interaction for Work (CHIWORK) · doi:10.1145/3808045.3808068
Yang, M.-Z., Kagawa, R., & Baba, Y. (2026) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3805689.3812288
Cakar, M., & Fischer, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31048-4_25
Azevedo, A. R., Gonçalves, F. B., & Brigas, J. M. F. (2026) · Journalism and Media · doi:10.3390/journalmedia7030137
Wijesinghe, N. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808362
CICEU, L.-D., Semenescu, A., & Rugiubei, R. (2026) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2026-0384
Cognitive Dependency and Pedagogical Resilience in AI-Saturated Classrooms
Alam, A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9484-8.ch007
Development and Effect Analysis of a Generative AI-Based Media Literacy Education Program
Cho, M., & Lee, Y. (2026) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2026.29.7.010
Beyond Manipulation: How Users Perceive Harmful AI Chatbot Interactions
Budde, V., & Toch, E. (2026) · Message Understanding Conference · doi:10.1145/3820253.3831388
Hou, Y., Yang, J., Wong, R. Y., & Howell, N. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816945
Liu, N. (2026) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3776591.3832500
ClearDose: When Personalized Human-AI Interactions Are Never Just Personal
Han, Y., cai, S., Li, X., & Leigh, S.-W. (2026) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/3785651.3831417
Stede, M., Schneider, J., & Wachsmuth, H. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-32291-3_11
- Authors
- Danry, V., Pataranutaporn, P., Groh, M., & Epstein, Z.
- Affiliations
- MIT Media Lab (US)
- Northwestern University (US)
- Stanford University (US)
- Venue
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-31
- OpenAlex
- W4409736075
- Cited by
- 35
- References
- 82
- Citations by year
- 2026: 30 · 2025: 5
- FWCI
- 117.58
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Social Sciences › Arts and Humanities › Philosophy › Epistemology, Ethics, and Metaphysics
- Keywords
- large language models, misinformation, belief revision, trust in AI, misinformation correction
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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