  Traditional discussions of bias in large language models focus on a
conception of bias closely tied to unfairness, especially as affecting
marginalized groups. Recent work raises the novel possibility of assessing the
outputs of large language models for a range of cognitive biases familiar from
research in judgment and decisionmaking. My aim in this paper is to draw two
lessons from recent discussions of cognitive bias in large language models:
cautious optimism about the prevalence of bias in current models coupled with
an anti-Panglossian willingness to concede the existence of some genuine biases
and work to reduce them. I draw out philosophical implications of this
discussion for the rationality of human cognitive biases as well as the role of
unrepresentative data in driving model biases.
