Understanding Racial Bias in Algorithms


This event is hosted by Moritz College of Law. This is not a Kirwan Institute sponsored event. 

This event kicks off the 2020-2021 Program on Data and Governance’s Data Points Lecture Series, sponsored by Porter Wright Morris & Arthur LLP.


We will make a recording available after the event.

About the Event

In the digital society, algorithms can be a major source of implicit bias and structural racism. Racial, gender, and other types of harmful bias can be baked into the algorithms that companies use to decide who gets jobs, loans, or insurance, and that government uses to determine who gets paroled or who is placed on the no-fly list. Identifying and preventing the racial bias in these algorithms is one of the key civil rights challenges of our era.

In this event, a panel of Ohio State professors and researchers will help us to understand racial bias in algorithms – what it is, how it happens, what harms result, and how we can work to eliminate it.

Dennis Hirsch | Professor of Law, Moritz College of Law, and Director, Program on Data and Governance

Tanya Berger-Wolf | Faculty Director, Translational Data Analytics Institute; Professor, Computer Science and Engineering; Evolution, Ecology, and Organismal Biology; and Electrical and Computer Engineering
Kelly Capatosto | Senior Data and Policy Specialist, Kirwan Institute for the Study of Race and Ethnicity
Sean Hill | Assistant Professor, Moritz College of Law
Christopher Stewart | Associate Professor, Computer Science and Engineering