How human–AI feedback loops alter human perceptual, emotional and social judgements
Open accessAbstract
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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In agentic systems, affective dynamics become more consequential because the system may plan, remember and act across tools, and human-AI feedback loops can amplify biases through repeated interaction.
“Together they form a recurrent loop in which affective cues can either stabilise collaboration or deepen dependence; in agentic systems, these dynamics become more consequential because the system may plan, remember and act across tools, and human-AI feedback loops can amplify biases through repeated interaction [25].”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [25]
The Main section and Discussion report that repeated interaction with biased AI can amplify human biases over time. The paper does not establish that this effect is more consequential in agentic systems because they can plan, remember, or act across tools. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI systems amplify human biases through iterative interactions, creating feedback loops that alter human judgment more significantly than human-human interactions.
“Glickman and Sharot (2024) shows how AI systems amplify human biases through iterative interactions, creating feedback loops that alter human judgment more significantly than human-human interactions.”
From The hidden functions of sycophancy in AI systems: steering, consistency, and cognitive dependency (2026) · cites this paper as Glickman and Sharot (2024)
Repeated interaction with biased judgements can shift human judgements toward the system's bias.
“repeated interaction with biased judgements can shift human judgements toward the system's bias [25];”
From Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration (2026) · cites this paper as [25]
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- Authors
- Glickman, M., & Sharot, T.
- Affiliations
- University College London (GB)
- Corresponding author
- Glickman, Moshe
- Sharot, Tali
- Venue
- Nature Human Behaviour
- Published
- 2024-12-18
- Volume
- 9
- Issue
- 2
- Pages
- 345-359
- ISSN
- 2397-3374 (ISSN-L)
- PMC
- PMC11860214
- OpenAlex
- W4405546422
- Cited by
- 289
- References
- 61
- Citations by year
- 2026: 183 · 2025: 96 · 2024: 3 · 2023: 7
- FWCI
- 116.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Human Behaviour (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- Wellcome Trust (214268/Z/18/Z)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Keywords
- human-AI interaction, feedback loops, algorithmic bias, perceptual judgment, social judgment, bias amplification, snowball effect, perceptions of AI
- MeSH terms
- Adult, Artificial Intelligence, Emotions, Feedback, Female, Humans, Judgment, Male, Social Perception, Young Adult
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