How human–AI feedback loops alter human perceptual, emotional & social judgments
How human–AI feedback loops alter human perceptual, emotional & social judgments
Dr. Moshe Glickman
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.
Bio:
Dr. Moshe Glickman is a Research Fellow at University College London's Department of Experimental Psychology and the Max Planck Centre for Computational Psychiatry and Ageing Research, working in Prof. Tali Sharot's Affective Brain Lab. He completed his PhD in Cognitive Psychology at Tel Aviv University, in Prof. Marius Usher's Computational Decision-Making Lab. Dr. Glickman's research lies at the intersection of cognitive science and artificial intelligence. His recent work focuses on how AI systems can amplify human biases, creating feedback loops that alter human decision-making. His findings demonstrate that AI-induced biases can significantly impact human judgment across perceptual, emotional and social contexts, with implications for ethical development and deployment of AI systems.
