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How human–AI feedback loops alter human perceptual, emotional and social judgements

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  • Moshe Glickman

    (University College London
    University College London)

  • Tali Sharot

    (University College London
    University College London
    Massachusetts Institute of Technology)

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.

Suggested Citation

  • Moshe Glickman & Tali Sharot, 2025. "How human–AI feedback loops alter human perceptual, emotional and social judgements," Nature Human Behaviour, Nature, vol. 9(2), pages 345-359, February.
    • Handle: RePEc:nat:nathum:v:9:y:2025:i:2:d:10.1038_s41562-024-02077-2
    DOI: 10.1038/s41562-024-02077-2
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    References listed on IDEAS

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