AI-based technologies used by cities and organizations have been shown to exhibit racial bias, and yet, they can determine who gets access to employment, care, and adequate housing. This project seeks to understand the racial disparities in AI-based systems and design and implement solutions in the areas of public safety, transportation, and health. Engagement with local government offices, public agencies, industry, and communities is at the center of its effort to tackle the challenge of achieving racially equitable AI.
Angela J. Haddad, Aupal Mondal, Chandra R. Bhat, Angie Zhang, Madison C. Liao, Lisa J. Macias, Min Kyung Lee, and S. Craig Watkins. “Pedestrian Crash Frequency: Unpacking the Effects of Contributing Factors and Racial Disparities.” Accident Analysis & Prevention 182 (March 1, 2023).