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.

翻译：社交媒体平台上的数字广告在塑造经济机会获取方面发挥着重要作用。本研究提出并实施了一种新的第三方审计方法，可用于评估教育机会广告投放中的种族偏见。第三方审计的重要性在于它允许外部机构验证社交媒体算法中是否存在偏见。教育领域具有法律保护以防止歧视，且存在种族定向的担忧，但广告投放算法引发的偏见此前尚未在该领域得到探索。先前审计已证明平台在住房和就业广告投放中存在歧视。这些审计结果为法律行动提供了依据，促使Meta修改其广告投放算法以减少偏见，但仅限于住房、就业和信贷领域。在本研究中，我们提出了一种新方法，用于测量平台教育广告投放算法中的种族歧视。我们将该方法应用于Meta平台，使用真实学校的广告进行测试，并观察投放结果。我们发现了Meta算法在教育机会广告投放中存在种族歧视的证据，这引发了法律和伦理担忧。我们的研究将算法歧视的证据扩展至教育领域，表明当前偏见缓解机制的范围有限，并建议在确保非歧视至关重要的领域，社交媒体第三方审计应发挥更广泛的作用。