Computer Science > Computers and Society
[Submitted on 3 Apr 2019 (v1), last revised 12 Sep 2019 (this version, v5)]
Title:Discrimination through optimization: How Facebook's ad delivery can lead to skewed outcomes
View PDFAbstract: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.
Submission history
From: Piotr Sapiezynski [view email][v1] Wed, 3 Apr 2019 16:40:41 UTC (1,652 KB)
[v2] Thu, 4 Apr 2019 17:24:01 UTC (1,652 KB)
[v3] Thu, 18 Apr 2019 14:21:01 UTC (3,321 KB)
[v4] Fri, 19 Apr 2019 00:42:16 UTC (1,653 KB)
[v5] Thu, 12 Sep 2019 17:46:16 UTC (1,659 KB)
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