How artificial intelligence can help combat systemic racism

MLK Visiting Professor S. Craig Watkins looks beyond algorithm bias to an AI future where models more effectively deal with systemic inequality.

In 2020, Detroit police arrested a Black man for shoplifting almost $4,000 worth of watches from an upscale boutique. He was handcuffed in front of his family and spent a night in lockup. After some questioning, however, it became clear that they had the wrong man. So why did they arrest him in the first place?

The reason: a facial recognition algorithm had matched the photo on his driver’s license to grainy security camera footage.

Facial recognition algorithms — which have repeatedly been demonstrated to be less accurate for people with darker skin — are just one example of how racial bias gets replicated within and perpetuated by emerging technologies.

“There’s an urgency as AI is used to make really high-stakes decisions,” says MLK Visiting Professor S. Craig Watkins, whose academic home for his time at MIT is the Institute for Data, Systems, and Society (IDSS). “The stakes are higher because new systems can replicate historical biases at scale.”

Watkins, a professor at the University of Texas at Austin and the founding director of the Institute for Media Innovation, researches the impacts of media and data-based systems on human behavior, with a specific concentration on issues related to systemic racism. “One of the fundamental questions of the work is: how do we build AI models that deal with systemic inequality more effectively?”

Ethical AI

Inequality is perpetuated by technology in many ways across many sectors. One broad domain is health care, where Watkins says inequity shows up in both quality of and access to care. […]