Computing for Good 2018: Implicit Bias Detection

Implicit Bias Detection: A Carnegie Mellon University Computing for Good 2018 Project

Implicit bias continues to be a persistent source of prejudice and discrimination across many domains. This pressing social issue has significant negative consequences, particularly for stereotype-targeted groups, such as women, people with disabilities, low socioeconomic status populations, and racial minority groups. The threat linked with being the target of bias has been shown to activate a variety of anxiety- and stress-related physiological responses, including changes in heart rate acceleration, skin conductance, body temperature, and blood pressure.
We are looking for solutions to link bias experiences with physiological responses to make people aware of their implicit bias. Using mobile and wearable sensors we are looking to detect occurrences of bias and the social contexts and locations in which the experience with bias are more likely to take place in the city of Pittsburgh.