Black Doctors Talk Podcast Episode 89 featuring Dr. Kalinda Ukanwa - Algorithmic Fairness and Bias

Join us on this week’s Black Doctors Talk Podcast which features our guest, Dr. Kalinda Ukanwa who speaks to us on the topics of algorithmic fairness and bias and algorithmic decision-making. Dr. Ukanwa is currently in her second career. Previously she was an industrial engineer, financial analyst, and finance executive at Walt Disney, Citigroup, Viacom, and Kaplan. Currently, Dr. Ukanwa is Assistant Professor of Marketing at the University of Southern California, a quantitative modeler, and an expert in artificial intelligence.

 

During the podcast, Dr. Ukanwa shares his insights on the latest equity fight that those in Black and Brown communities must battle: technology. Technology, such as artificial intelligence (AI) is used to make decisions about consumers, however Dr. Ukanwa shares her legitimate concerns about the errors that occur related to AI inability to accurately identify and inform decisions for all consumers. Dr. Ukanwa shares her love for teaching and leading, but also enjoys one of the biggest advantages of being a professor, which is the ability to engage in passion work via research. She discusses how she uses research to collect data and challenge how AI can be misleading and problematic. While valuing the use and benefits of technology, Dr. Ukanwa boldly stands for equity and fairness and advocates that her colleagues from other races and cultures consider everyone in their research developments and methodology to also address those from all races, cultures, and genders.

 

Dr. Ukanwa was the winner of the 2018 Eli Jones Promising Young Scholar Award, a finalist for the 2018 INFORMS Service Science Best Student Paper Award. 2019 Howard/AMA Doctoral Dissertation Award, and the 2020 AMS Mary Kay Doctoral Dissertation Award. She is a wife and mother, but most importantly an intelligent and powerful woman with so much to offer the academia. Dr. Ukanwa earned a Ph.D. in Marketing (Quantitative) from University of Maryland; M.B.A., B.S. and M.S. in Industrial Engineering from Stanford University.