Detecting social biases using mental state inference

Social biases are prevalent in everyday social interactions, but they are often expressed in subtle ways that can make them difficult to detect. Yet, intuitively, people can often recognize when they are the subject of a bias, even in the absence of any overt behavior. How do we do this? While much research has focused on the negative consequences of being the subject of a bias, less is known about the cognitive mechanisms that allow people to explicitly detect biases in the first place. In this paper, we propose an account of bias detection which is grounded on mental state representations. We propose that people infer biases by detecting a gap between expected unbiased behavior and observed real-world behavior, which in turns reveals the hidden biases influencing other people's beliefs. We present a formal computational model of this account and, across three preregistered studies (n=720 total), we show that this model captures participants' inferences about an observer's prior beliefs (Experiment 1), general social biases (Experiment 2), and specific real-world biases (Experiments 3a--3c). Moreover, our model captures key patterns of variance in participant responses which simpler alternative models fail to capture. These findings highlight the role of Theory of Mind in social bias detection, and broaden our understanding of the human capacity to detect and reason about implicit prejudices.