Language Models Embody and Amplify Human Cognitive Distortions: What Is to Be Done?

2026-07-22

AI Summary

This study demonstrates that large language models do not merely reflect human cognitive biases implicitly during alignment but systematically amplify them and propagate these amplified biases back to humans, thereby threatening fairness and safety in decision-making. Challenging the prevailing assumption that AI bias is a passive mirror of human prejudice, this work establishes for the first time that such models act as active amplifiers of bias—a phenomenon that intensifies across successive model generations. Drawing on social cognitive theory and behavioral analyses of cross-generational models, the research develops a diagnostic framework that elucidates the mechanisms underlying the隐蔽ness, amplification, and transmissibility of AI-induced bias. Building on these insights, the study proposes a multi-layered intervention strategy spanning diagnostic, regulatory, and operational dimensions to mitigate the adverse societal impacts of AI bias on human judgment and policy decisions.

Abstract

Human judgment is fundamentally prone to error. A promise of AI is that it will rid decisions of bias and ensure a fairer and safer world for all. Yet research unequivocally demonstrates that LLMs exhibit consequential sociocognitive biases. We alert readers that bias in AI (a) is covert and ironically a feature of alignment goals, (b) is not merely a mirror, but an amplifier of human bias, (c) intensifies across model generations, and (d) even transmits bias to humans. Given the potentially seismic and ubiquitous influence of AI on decision making, we propose countermeasures that are diagnostic, regulatory and operational.

Problem

• cognitive distortions
• sociocognitive biases
• LLMs
• AI bias
• amplification of bias

Innovation

• cognitive distortions
• language models
• bias amplification
• AI alignment
• sociocognitive bias