Detection of Racial Bias from Physiological Responses

TL;DR

This study demonstrates that racial bias can be reliably detected from physiological responses such as heart rate and skin conductance, achieving over 76% accuracy using machine learning analysis.

Contribution

It introduces a novel approach to identify implicit racial bias through physiological signals, highlighting EDA as a key indicator.

Findings

Physiological signals can predict racial bias with 76.1% accuracy.

Skin conductance response (EDA) correlates strongly with racial bias.

Significant differences in EDA features exist between biased and unbiased individuals.

Abstract

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.