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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.
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This content will become publicly available on March 18, 2026
Seeing Ad Transparency More Clearly: Public Perception, Use of Ad Transparency Tools, and Responses to Dataveillance
Advertising transparency efforts aim to facilitate user knowledge and control over the use of their data for personalized advertising. We seek to understand the perceptions and use of ad transparency tools among U.S. adults and whether ad transparency information surfaced by such tools affects public perceptions of online surveillance. Results show that people are aware of, although not knowledgeable about, such tools and feel slightly satisfied with them but do not use them often. Drawing from the dataveillance effects in advertising landscape (DEAL) framework, we demonstrate that whereas exposure to no, less, or more extensive transparency information did not change perceived surveillance, exposure to more extensive targeting information increased knowledge about ad transparency efforts and self-efficacy in using transparency tools. Moreover, perceived surveillance is associated with perceived benefits and risks of personalized advertising, interest in ad transparency information, desire for privacy regulation, negative affect, and intention to protect privacy and use ad transparency tools. These relationships were moderated by privacy concern and privacy cynicism. Theoretically, this study offers an empirical test of key tenets of the DEAL framework and contributes to policy debates about ad transparency.
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- Award ID(s):
- 2151340
- PAR ID:
- 10578743
- Publisher / Repository:
- Routledge Taylor & Francis Group
- Date Published:
- Journal Name:
- Journal of Advertising
- ISSN:
- 0091-3367
- Page Range / eLocation ID:
- 1 to 20
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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