Challenging the appearance of machine intelligence: Cognitive bias in LLMs
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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.
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