Detection of Racial Bias from Physiological Responses
This addresses the issue of unconscious racial bias detection for social science and psychology, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of detecting implicit racial bias by analyzing physiological responses, achieving 76.1% accuracy in prediction using data from 46 subjects.
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.