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. We call for stronger education, risk management, and continued research as widespread adoption of this technology increases. Finally, we close with a set of best practices for when and how to employ this technology as widespread adoption continues to grow.

翻译：对大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，文献记载的180多种渗透人类推理与决策的认知偏差，在讨论人工智能伦理复杂性时却常被忽视。我们证明了大语言模型中存在这些认知偏差，并探讨了以专业知识为幌子使用偏差推理的后果。随着该技术的广泛普及，我们呼吁加强教育、风险管理和持续研究。最后，针对该技术持续增长的应用规模，我们提出了一套关于何时及如何采用该技术的最佳实践指南。