Assessments of algorithmic bias in large language models (LLMs) are generally catered to uncovering systemic discrimination based on protected characteristics such as sex and ethnicity. However, there are over 180 documented cognitive biases that pervade human reasoning and decision making that are routinely ignored when discussing the ethical complexities of AI. We demonstrate the presence of these cognitive biases in LLMs and discuss the implications of using biased reasoning under the guise of expertise. Rapid adoption of LLMs has brought about a technological shift in which these biased outputs are pervading more sectors than ever before. We call for stronger education, risk management, and continued research as widespread adoption of this technology increases.

翻译：对大型语言模型算法偏差的评估通常旨在揭示基于性别、种族等受保护特征的系统性歧视。然而，人类推理与决策中普遍存在的180余种已记录的认知偏差，在探讨人工智能伦理复杂性时却常被忽视。我们证明了这些认知偏差在大型语言模型中的存在，并探讨了以专业知识为幌子使用有偏差推理的潜在影响。大型语言模型的快速普及引发了技术变革，使得这些有偏差的输出以前所未有的规模渗透至更多领域。随着该技术的广泛采用，我们呼吁加强教育、风险管理及持续研究。