ArticleNature human behaviour2025
How human-AI feedback loops alter human perceptual, emotional and social judgements.
Article in Nature human behaviour, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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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.
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Who cites it
33 citing papers in PubMed.
- AI and the Reproduction of Health Inequity: Redistribution-Translation-Accumulation Framework.Journal of medical Internet research · 2026Article
- Generative AI emotion recognition from bodily gestures and vocal tone reveals modality-specific performance and positivity bias.Scientific reports · 2026Article
- How large language models judge and influence human cooperation.PNAS nexus · 2026Article
- A clinically validated framework for auditing AI chatbot behavior in mental health interactions.Nature medicine · 2026Article
- Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering.Nature neuroscience · 2026Article
- Embodied cognition-driven interpretable trajectory prediction of autonomous systems.Nature communications · 2026Article
- Why sycophantic LLMs may imperil interactive norms between humans.Communications psychology · 2026Review
- Characterizing the spiral: potential mechanisms in AI-associated delusions.NPP - digital psychiatry and neuroscience · 2026Review
- Warning people about the risk of AI error mitigates human acquisition of AI bias.Cognitive research: principles and implications · 2026Article
- Biased AI writing assistants shift users' attitudes on societal issues.Science advances · 2026Article
- Navigating Complexity in Reflexive Cybernetics: Forces, feedback, and felt valences.Integrative psychological & behavioral science · 2026Article
- TechnologicalNature. Mental health · 2026Article
- Treatment Response Phenotyping Informed by Patient Physiologic Characteristics Could Drive Precision Critical Care Through Augmented Intelligence: A Narrative Review.CHEST critical care · 2026Article
- Artificial Intelligence and the Psychology of Human Connection.Perspectives on psychological science : a journal of the Association for Psychological Science · 2026Article
- An Operational Framework for Affect-Adjacent Structure in Plant-Environment Interaction.Bioengineering (Basel, Switzerland) · 2026Article
- The impact of generative AI on social media: an experimental study.Scientific reports · 2026Article
- Disagreement between human and AI evaluation of treatment plans.Scientific reports · 2026Article
- Decoding dynamic emotional valence in GenAI interactions: insights from covariate-dependent Markov chains.Frontiers in psychology · 2026Article
- Positive psychology 4.0: a science of consequence for a world that can no longer wait.Frontiers in psychology · 2026Article
- Generative AI as a de facto mental health provider: a policy brief and urgent call for regulation.Frontiers in digital health · 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
2 authors.
Funding
Abstract
Artificial intelligence (AI) technologies are rapidly advancing, enhancing human capabilities across various fields spanning from finance to medicine. Despite their numerous advantages, AI systems can exhibit biased judgements in domains ranging from perception to emotion. Here, in a series of experiments (n = 1,401 participants), we reveal a feedback loop where human-AI interactions alter processes underlying human perceptual, emotional and social judgements, subsequently amplifying biases in humans. This amplification is significantly greater than that observed in interactions between humans, due to both the tendency of AI systems to amplify biases and the way humans perceive AI systems. Participants are often unaware of the extent of the AI's influence, rendering them more susceptible to it. These findings uncover a mechanism wherein AI systems amplify biases, which are further internalized by humans, triggering a snowball effect where small errors in judgement escalate into much larger ones.
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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.