  Despite the evolution of norms and regulations to mitigate the harm from
biases, harmful discrimination linked to an individual's unconscious biases
persists. Our goal is to better understand and detect the physiological and
behavioral indicators of implicit biases. This paper investigates whether we
can reliably detect racial bias from physiological responses, including heart
rate, conductive skin response, skin temperature, and micro-body movements. We
analyzed data from 46 subjects whose physiological data was collected with
Empatica E4 wristband while taking an Implicit Association Test (IAT). Our
machine learning and statistical analysis show that implicit bias can be
predicted from physiological signals with 76.1% accuracy. Our results also show
that the EDA signal associated with skin response has the strongest correlation
with racial bias and that there are significant differences between the values
of EDA features for biased and unbiased participants.
