# Why am I Still Seeing This: Measuring the Effectiveness Of Ad Controls and Explanations in AI-Mediated Ad Targeting Systems

arXiv: https://arxiv.org/abs/2408.11910 · HTML: https://mlsanity.com/paper/2408.11910/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination](https://mlsanity.com/paper/1408.6491/) (2014)
- [On the Trade-Off between Actionable Explanations and the Right to be Forgotten](https://mlsanity.com/paper/2208.14137/) (2022)
- [Auditing for Discrimination in Algorithms Delivering Job Ads](https://mlsanity.com/paper/2104.04502/) (2021)
- [Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted Advertising](https://mlsanity.com/paper/2308.15768/) (2023)
- [On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook](https://mlsanity.com/paper/1808.09218/) (2018)
- [The Distressing Ads That Persist: Uncovering The Harms of Targeted Weight-Loss Ads Among Users with Histories of Disordered Eating](https://mlsanity.com/paper/2204.03200/) (2022)
- [Facebook's Advertising Platform: New Attack Vectors and the Need for Interventions](https://mlsanity.com/paper/1803.10099/) (2018)
- [Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging](https://mlsanity.com/paper/1912.04255/) (2019)
- [Exploring the Online Micro-targeting Practices of Small, Medium, and Large Businesses](https://mlsanity.com/paper/2207.09286/) (2022)
- [Problematic Advertising and its Disparate Exposure on Facebook](https://mlsanity.com/paper/2306.06052/) (2023)
- [Discrimination through Image Selection by Job Advertisers on Facebook](https://mlsanity.com/paper/2306.07527/) (2023)
- [Auditing for Racial Discrimination in the Delivery of Education Ads](https://mlsanity.com/paper/2406.00591/) (2024)

## Built on by (most cited first, 1 of 1)

- [MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers](https://mlsanity.com/paper/2602.00933/) (2026)

## Abstract (arXiv)

Recently, Meta has shifted towards AI-mediated ad targeting mechanisms that do not require advertisers to provide detailed targeting criteria, likely driven by excitement over AI capabilities as well as new data privacy policies and targeting changes agreed upon in civil rights settlements. At the same time, Meta has touted their ad preference controls as an effective mechanism for users to control the ads they see. Furthermore, Meta markets their targeting explanations as a transparency tool that allows users to understand why they saw certain ads and inform actions to control future ads. Our study evaluates the effectiveness of Meta's "See less" ad control and the actionability of ad targeting explanations following the shift to AI-mediated targeting. We conduct a large-scale study, randomly assigning participants to mark "See less" to Body Weight Control or Parenting topics, and collecting the ads and targeting explanations Meta shows to participants 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 marked to "See less" of continued to be delivered. We hypothesize that the poor effectiveness of controls and lack of actionability in explanations are the result of the shift to AI-mediated targeting, for which explainability and transparency tools have not yet been developed. Our work thus provides evidence for the need of new methods for transparency and user control, suitable and reflective of increasingly complex AI-mediated ad delivery systems.