Discrimination through Optimization
By Muhammad Ali, Piotr Sapiezynski, Aleksandra Korolova, Alan Mislove, Miranda Bogen. 2019
231 citations recorded by the source corpus.
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.
Subfields: Marketing, Sociology and Political Science, Strategy and Management, AI Ethics, Fairness & Society, Mathematical Optimization
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Citations in the corpus
- Between Subjectivity and Imposition (2020, background): “However, previous research has shown that they may contain biases that lead to discrimination and exclusion in several domains such as credit [37], the job market [70], facial recognition systems [19, 45, 71], algorithmic ltering [4, 62], and even advertisement [1].”
- Algorithmic Bias in Education (2021, background): “Some examples include gender and racial bias in ad delivery for housing and employment (Ali et al., 2019; Benner et al., 2019); gender bias in assigning credit limits (Knight, 2019; Telford, 2019); racial bias in sentencing decisions (Angwin et al., 2016), racial bias in identifying patients for…”
- Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development (2020, background): “For instance, Facebook let firms choose among over 100 well-defined attributes (Ali et al. 2019).”
- Awareness in practice: tensions in access to sensitive attribute data for antidiscrimination (2019, background): “n. However, it is difficult to imagine an approach that would avoid these questions. • Facebook, in delivering advertisements on its platform, introduces demographic skews along gender and race lines [5]. This practice is currently being challenged in court by the Awareness in Practice: Tensions in ”