ArticleScientific reports2026
Examining human reliance on artificial intelligence in decision making.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Cognitive Performance Under AI Advice: Development and Initial Validation of a CHC-Informed Assessment for Organizational Decision-Making.Journal of Intelligence · 2026Article
- Artificial Intelligence in Kidney Transplant Pathology: Current Evidence, Limitations, and Relevance to the Banff Framework.Kidney international reports · 2026Review
- Human detection of AI-generated faces and voices is not domain-general.Scientific reports · 2026Article
- What might Confucius say? Digital doppelgängers for people who are newly dead, dying, or comatose.Global bioethics = Problemi di bioetica · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of Artificial Intelligence (AI) to effectively support human decision making depends on whether humans are willing to trust in, and thus rely on, AI. Understanding human reliance on AI is critical given controversial reports of AI inaccuracy and bias. Furthermore, the erroneous belief that using technology removes biases may lead to overreliance on AI. To examine humans’ reliance on AI, human participants (N = 295, Mage = 33.79) judged the authenticity of 80 faces (40 real, 40 AI-synthesized) presented alongside guidance supposedly from humans or from AI. This guidance was correct only half of the time. Participants indicated their confidence in each judgement and completed measures to examine propensity to trust humans and general attitudes towards AI. Participants who received AI guidance and exhibited more positive attitudes towards AI showed poorer discriminability between real and synthetic faces than those with less positive attitudes towards AI. For participants who received human guidance, level of trust in humans did not affect discriminability. Therefore, AI-derived guidance may be uniquely placed to engender biases in humans, leading to less effective decision making. To ensure successful human-AI decision making partnerships, more research is needed to understand precisely how humans use AI guidance in various contexts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.