Cognitive Bias in High-Stakes Decision-Making with LLMs
Jessica Echterhoff, Yao Liu, Abeer Alessa, Julian McAuley, Zexue He
Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks. However, given their training on human (created) data, LLMs can inherit both societal biases against protected groups, as well as be subject to cognitive bias. Such 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 sciences, we develop a dataset containing 16,800 prompts to evaluate different cognitive biases (e.g., prompt-induced, sequential, inherent). We test various bias mitigation strategies, amidst proposing a novel method using LLMs to debias their own prompts. Our analysis provides a comprehensive picture on the presence and effects of cognitive bias across different commercial and open-source models. We demonstrate that our self-help debiasing effectively mitigate cognitive bias without having to manually craft examples for each bias type.