  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 propose a
new methodology that allows us to measure racial discrimination in a platform's
ad delivery algorithms for education ads. We apply our method to Meta using ads
for real schools and observe the results of delivery. We find evidence of
racial discrimination in Meta's algorithmic delivery of ads for education
opportunities, posing legal and ethical concerns. Our results extend evidence
of algorithmic discrimination to the education domain, showing that current
bias mitigation mechanisms are narrow in scope, and suggesting a broader role
for third-party auditing of social media in areas where ensuring
non-discrimination is important.
