Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
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Abstract (arXiv)
The enormous financial success of online advertising platforms is partially due to the precise targeting features they offer. Although researchers and journalists have found many ways that advertisers can target---or exclude---particular groups of users seeing their ads, comparatively little attention has been paid to the implications of the platform's ad delivery process, comprised of the platform's choices about which users see which ads. It has been hypothesized that this process can "skew" ad delivery in ways that the advertisers do not intend, making some users less likely than others to see particular ads based on their demographic characteristics. In this paper, we demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platform's own predictions about the "relevance" of ads to different groups of users. We find that both the advertiser's budget and the content of the ad each significantly contribute to the skew of Facebook's ad delivery. Critically, we observe significant skew in delivery along gender and racial lines for "real" ads for employment and housing opportunities despite neutral targeting parameters. Our results demonstrate previously unknown mechanisms that can lead to potentially discriminatory ad delivery, even when advertisers set their targeting parameters to be highly inclusive. This underscores the need for policymakers and platforms to carefully consider the role of the ad delivery optimization run by ad platforms themselves---and not just the targeting choices of advertisers---in preventing discrimination in digital advertising.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Apr 2019.
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Citing papers per year: 2021: 3; 2022: 5; 2023: 4; 2024: 1; 2025: 2.
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12 most cited of 15 All 15 (JSON)
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- 2024Auditing of AI: Legal, Ethical and Technical Approaches7 Jul 2024
- 2023Unmasking Nationality Bias: A Study of Human Perception of Nationalities in AI-Generated Articles8 Aug 2023
- 2023Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning5 Jun 2023
- 2023How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?27 Feb 2023
- 2023Human-Centric Multimodal Machine Learning: Recent Advances and Testbed on AI-based Recruitment13 Feb 2023
- 2022FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data13 Dec 2022
- 2022Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning21 Sep 2022
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- 2022Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits13 May 2022
- 2022Data Representativity for Machine Learning and AI Systems9 Mar 2022
12 newest of 15 All 15 (JSON)