  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.
