Online behavioral advertising can be discriminatory, so what’s the plan to fix this? | The TechTank Podcast

Social media companies like Facebook are being called out on the racial discrimination appearing in and resulting from their machine-learning algorithms. Advertising platforms use these models to target campaigns and consumers for housing, banking, and employment opportunities, but the results are not often fair. For some populations, they can result in the foreclosure on economic, social, and political opportunities that can have both short- and long-term effects on their livelihood. In this episode of the TechTank podcast, host Nicol Turner Lee is joined by Jinyan Zang, fellow at the Public Interest Tech Lab at the Harvard Kennedy School and author of a forthcoming report examining Facebook’s ad model, and Dominique Harrison, Director of the Technology Policy Program at the Joint Center for Political and Economic Studies to discuss how online behavioral advertising can perpetuate racial discrimination and what can be done to remedy such biases.

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TechTank is a biweekly podcast from The Brookings Institution exploring the most consequential technology issues of our time. From artificial intelligence and racial bias in algorithms, to Big Tech, the future of work, and the digital divide, TechTank takes abstract ideas and makes them accessible. Moderators Dr. Nicol Turner Lee and Darrell West speak with leading technology experts and policymakers to share new data, ideas, and policy solutions to address the challenges of our new digital world.