Designing AI to Advance Racial Equity

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.

Team Members

S. Craig Watkins

School of Journalism and Media

Chandra Bhat

Civil, Architectural and Environmental Engineering

Min Kyung Lee

School of Information

DeLawnia Comer-HaGans

Dell Med School

Sherri Greenberg

LBJ School of Public Affairs

Matt Kammer-Kerwick

IC2 Institute

Alex Karner

School of Architecture

Kenneth A. Perrine

Center for Transportation Research

Sharon Ricks

Dell Med School

News

Nov. 1, 2021

Designing AI for Racial Equity: Translating Ethics into Practice

Among the principles for ethical and responsible AI none is more prominent than the “fairness and non-discrimination” principle, that is, the idea that data-based systems like AI/ML should avoid unjust impacts on people, especially those from historically marginalized populations.

Sept. 28, 2021

Building Equity in AI: Insights from The University of Texas and Microsoft

S. Craig Watkins, professor in the Moody College of Communications, shares guiding strategies and principles for approaching AI and its role in society, specifically on how to use AI technology to solve health, humanitarian, and accessibility issues while keeping transparency, equity, and ethics at the forefront.