Cognitive Bias in Decision-Making with LLMs

Abstract

Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks.Given their training on human (created) data, LLMs have been shown to inherit societal biases against protected groups, as well as be subject to bias functionally resembling cognitive bias.Human-like bias can impede fair and explainable decisions made with LLM assistance.Our work introduces BIASBUSTER, a framework designed to uncover, evaluate, and mitigate cognitive bias in LLMs, particularly in high-stakes decision-making tasks.Inspired by prior research in psychology and cognitive science, we develop a dataset containing 13,465 prompts to evaluate LLM decisions on different cognitive biases (e.g., prompt-induced, sequential, inherent) 1 .We test various bias mitigation strategies, while proposing a novel method utilizing LLMs to debias their own human-like cognitive bias within prompts.Our analysis provides a comprehensive picture of the presence and effects of cognitive bias across commercial and open-source models.We demonstrate that our selfhelp debiasing effectively mitigates model answers that display patterns akin to human cognitive bias without having to manually craft examples for each bias.

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