AI-induced hyper-learning in humans

Glickman, Moshe; Sharot, Tali

doi:10.1016/j.copsyc.2024.101900

Current Opinion in Psychology

2024

DOI: 10.1016/j.copsyc.2024.101900

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AI-induced hyper-learning in humans

Moshe Glickman,

Tali Sharot

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2024

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

Glickman,

Sharot

2024

Nat Hum Behav

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

Glickman,

Sharot

2024

Nat Hum Behav

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AI-induced hyper-learning in humans

Cited by 1 publication

References 30 publications

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

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

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