Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups. We propose metacognitive myopia as a cognitive-ecological framework accounting for a conglomerate of established and emerging LLM biases. Our theoretical framework posits that biased samples in the information environment cause five symptoms of metacognitive myopia in LLMs: integration of invalid embeddings, susceptibility to redundant information, neglect of base rates in conditional computation, decision rules based on frequency, and inappropriate higher-order statistical inference for nested data structures. Moreover, it posits that the two main components of metacognition, monitoring and control, could account for these five symptoms. Accordingly, we further outline how monitoring and control could be approximated technically, for instance, through hidden parallel reasoning histories that allow interactive LLMs to evaluate risks of myopic inference before generating overt responses. Our theoretical framework provides a novel perspective on flawed human-machine interactions and agentic AI and raises significant ethical concerns regarding the implementation of LLMs in organizational structures and high-stakes decisions.

翻译：大型语言模型（LLM）表现出潜在的有害偏见，这些偏见强化了文化嵌入的刻板印象，影响了道德判断，或放大了对多数群体的积极评价。我们提出元认知短视作为一个认知生态学框架，用以解释已确立和新出现的LLM偏见。我们的理论框架假设，信息环境中的有偏样本导致LLM出现五种元认知短视症状：无效嵌入的整合、对冗余信息的敏感性、条件计算中对基率的忽视、基于频率的决策规则，以及对嵌套数据结构的不恰当高阶统计推断。此外，该框架提出，元认知的两个主要组成部分——监控与控制——可以解释这五种症状。据此，我们进一步概述了如何从技术上近似实现监控与控制，例如通过隐藏的并行推理历史，使交互式LLM在生成显性响应前评估短视推理的风险。我们的理论框架为有缺陷的人机交互和自主AI提供了新视角，并就LLM在组织结构和高风险决策中的实施提出了重大的伦理关切。