Auditing for Racial Discrimination in the Delivery of Education Ads
Basileal Imana, Aleksandra Korolova, John Heidemann

TL;DR
This paper introduces a new third-party auditing method to detect racial bias in social media ad delivery, specifically focusing on education ads, revealing discrimination that raises legal and ethical concerns.
Contribution
It develops a novel methodology for measuring racial bias in ad delivery algorithms within the education domain, extending prior work beyond housing and employment.
Findings
Evidence of racial discrimination in Meta's education ad delivery
Current bias mitigation measures are limited in scope
Highlights need for broader third-party auditing in social media
Abstract
Digital ads on social-media platforms play an important role in shaping access to economic opportunities. Our work proposes and implements a new third-party auditing method that can evaluate racial bias in the delivery of ads for education opportunities. Third-party auditing is important because it allows external parties to demonstrate presence or absence of bias in social-media algorithms. Education is a domain with legal protections against discrimination and concerns of racial-targeting, but bias induced by ad delivery algorithms has not been previously explored in this domain. Prior audits demonstrated discrimination in platforms' delivery of ads to users for housing and employment ads. These audit findings supported legal action that prompted Meta to change their ad-delivery algorithms to reduce bias, but only in the domains of housing, employment, and credit. In this work, we…
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