skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on November 8, 2025

Title: On the Use of Proxies in Political Ad Targeting
Detailed targeting of advertisements has long been one of the core offerings of online platforms. Unfortunately, malicious advertisers have frequently abused such targeting features, with results that range from violating civil rights laws to driving division, polarization, and even social unrest. Platforms have often attempted to mitigate this behavior by removing targeting attributes deemed problematic, such as inferred political leaning, religion, or ethnicity. In this work, we examine the effectiveness of these mitigations by collecting data from political ads placed on Facebook in the lead up to the 2022 U.S. midterm elections. We show that major political advertisers circumvented these mitigations by targeting proxy attributes: seemingly innocuous targeting criteria that closely correspond to political and racial divides in American society. We introduce novel methods for directly measuring the skew of various targeting criteria to quantify their effectiveness as proxies, and then examine the scale at which those attributes are used. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising and emphasize the urgency for increased transparency.  more » « less
Award ID(s):
2318290 2344925 1956435
PAR ID:
10565426
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Association for Computing Machinery
Date Published:
Journal Name:
Proceedings of the ACM on Human-Computer Interaction
Volume:
8
Issue:
CSCW2
ISSN:
2573-0142
Page Range / eLocation ID:
1 to 31
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Proceedings of the 14th ACM International Conference on Web Search and Data Mining)
    null (Ed.)
    Political campaigns are increasingly turning to targeted advertising platforms to inform and mobilize potential voters. The appeal of these platforms stems from their promise to empower advertisers to select (or "target") users who see their messages with great precision, including through inferences about those users' interests and political affiliations. However, prior work has shown that the targeting may not work as intended, as platforms' ad delivery algorithms play a crucial role in selecting which subgroups of the targeted users see the ads. In particular, the platforms can selectively deliver ads to subgroups within the target audiences selected by advertisers in ways that can lead to demographic skews along race and gender lines, and do so without the advertiser's knowledge. In this work we demonstrate that ad delivery algorithms used by Facebook, the most advanced targeted advertising platform, shape the political ad delivery in ways that may not be beneficial to the political campaigns and to societal discourse. In particular, the ad delivery algorithms lead to political messages on Facebook being shown predominantly to people who Facebook thinks already agree with the ad campaign's message even if the political advertiser targets an ideologically diverse audience. Furthermore, an advertiser determined to reach ideologically non-aligned users is non-transparently charged a high premium compared to their more aligned competitor, a difference from traditional broadcast media. Our results demonstrate that Facebook exercises control over who sees which political messages beyond the control of those who pay for them or those who are exposed to them. Taken together, our findings suggest that the political discourse's increased reliance on profit-optimized, non-transparent algorithmic systems comes at a cost of diversity of political views that voters are exposed to. Thus, the work raises important questions of fairness and accountability desiderata for ad delivery algorithms applied to political ads. 
    more » « less
  2. ; ; ; ; (, USENIX Security Symposium 2023)
    Targeted advertising remains an important part of the free web browsing experience, where advertisers' targeting and personalization algorithms together find the most relevant audience for millions of ads every day. However, given the wide use of advertising, this also enables using ads as a vehicle for problematic content, such as scams or clickbait. Recent work that explores people's sentiments toward online ads, and the impacts of these ads on people's online experiences, has found evidence that online ads can indeed be problematic. Further, there is the potential for personalization to aid the delivery of such ads, even when the advertiser targets with low specificity. In this paper, we study Facebook--one of the internet's largest ad platforms--and investigate key gaps in our understanding of problematic online advertising: (a) What categories of ads do people find problematic? (b) Are there disparities in the distribution of problematic ads to viewers? and if so, (c) Who is responsible--advertisers or advertising platforms? To answer these questions, we empirically measure a diverse sample of user experiences with Facebook ads via a 3-month longitudinal panel. We categorize over 32,000 ads collected from this panel (n = 132); and survey participants' sentiments toward their own ads to identify four categories of problematic ads. Statistically modeling the distribution of problematic ads across demographics, we find that older people and minority groups are especially likely to be shown such ads. Further, given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms (advertising platforms themselves) played a significant role in the biased distribution of these ads. 
    more » « less
  3. ; (, Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society)
    Recently, Meta has shifted towards AI-mediated ad targeting mechanisms that do not require advertisers to provide detailed targeting criteria. The shift is likely driven by excitement over AI capabilities as well as the need to address new data privacy policies and targeting changes agreed upon in civil rights settlements. At the same time, in response to growing public concern about the harms of targeted advertising, Meta has touted their ad preference controls as an effective mechanism for users to exert control over the advertising they see. Furthermore, Meta markets their Why this ad targeting explanation as a transparency tool that allows users to understand the reasons for seeing particular ads and inform their actions to control what ads they see in the future. Our study evaluates the effectiveness of Meta's See less ad control, as well as the actionability of ad targeting explanations following the shift to AI-mediated targeting. We conduct a large-scale study, randomly assigning participants the intervention of marking See less to either Body Weight Control or Parenting topics, and collecting the ads Meta shows to participants and their targeting explanations before and after the intervention. We find that utilizing the See less ad control for the topics we study does not significantly reduce the number of ads shown by Meta on these topics, and that the control is less effective for some users whose demographics are correlated with the topic. Furthermore, we find that the majority of ad targeting explanations for local ads made no reference to location-specific targeting criteria, and did not inform users why ads related to the topics they requested to See less of continued to be delivered. We hypothesize that the poor effectiveness of controls and lack of actionability and comprehensiveness in explanations are the result of the shift to AI-mediated targeting, for which explainability and transparency tools have not yet been developed by Meta. Our work thus provides evidence for the need of new methods for transparency and user control, suitable and reflective of how the increasingly complex and AI-mediated ad delivery systems operate. 
    more » « less
  4. ; ; (, Proceedings of the ACM on Human-Computer Interaction)
    Social media platforms make trade-offs in their design and policy decisions to attract users and stand out from other platforms. These decisions are influenced by a number of considerations, e.g. what kinds of content moderation to deploy or what kinds of resources a platform has access to. Their choices play into broader political tensions; social media platforms are situated within a social context that frames their impact, and they can have politics through their design that enforce power structures and serve existing authorities. We turn to Pillowfort, a small social media platform, to examine these political tensions as a case study. Using a discourse analysis, we examine public discussion posts between staff and users as they negotiate the site's development over a period of two years. Our findings illustrate the tensions in navigating the politics that users bring with them from previous platforms, the difficulty of building a site's unique identity and encouraging commitment, and examples of how design decisions can both foster and break trust with users. Drawing from these findings, we discuss how the success and failure of new social media platforms are impacted by political influences on design and policy decisions. 
    more » « less
  5. ; ; ; ; (, Proceedings of the 1st Conference on Fairness, Accountability and Transparency, PMLR)
    We explore ways in which discrimination may arise in the targeting of job-related advertising, noting the potential for multiple parties to contribute to its occurrence. We then examine the statutes and case law interpreting the prohibition on advertisements that indicate a preference based on protected class, and consider its application to online advertising. We focus on its interaction with Section 230 of the Communications Decency Act, which provides interactive computer services with immunity for providing access to information created by a third party. We argue that such services can lose that immunity if they target ads toward or away from protected classes without explicit instructions from advertisers to do so. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email