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：关于大型语言模型中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，在讨论人工智能的伦理复杂性时，有超过180种已记录的认知偏差被系统性地忽略，这些偏差普遍存在于人类的推理和决策过程中。我们证明了大型语言模型中存在这些认知偏差，并探讨了在专业伪装下使用有偏差推理的含义。随着这项技术的广泛普及，我们呼吁加强教育、风险管理和持续研究。最后，我们以一套最佳实践作为总结，指导在持续普及的过程中如何以及何时使用这项技术。