  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 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
utilising LLMs to debias their own 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 self-help debiasing
effectively mitigates model answers that display patterns akin to human
cognitive bias without having to manually craft examples for each bias.
