mlsanityData to 7 Oct 2026

Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination

arXiv 1408.6491 ↗First posted 27 Aug 2014cs.CRcited by 83

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Abstract (arXiv)

To partly address people's concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google's ads, and Ad Settings interact. AdFisher can run browser-based experiments and analyze data using machine learning and significance tests. Our tool uses a rigorous experimental design and statistical analysis to ensure the statistical soundness of our results. We use AdFisher to find that the Ad Settings was opaque about some features of a user's profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse changed the ads shown but not the settings page. We also found that setting the gender to female resulted in getting fewer instances of an ad related to high paying jobs than setting it to male. We cannot determine who caused these findings due to our limited visibility into the ad ecosystem, which includes Google, advertisers, websites, and users. Nevertheless, these results can form the starting point for deeper investigations by either the companies themselves or by regulatory bodies.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Aug 2014.

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Citing papers per year: 2021: 21; 2022: 21; 2023: 12; 2024: 16; 2025: 8; 2026: 5.

83 papers.

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  11. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  12. 2021SAR-Net: A Scenario-Aware Ranking Network for Personalized Fair Recommendation in Hundreds of Travel Scenarioscited by 23
38 more
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  5. 2022Algorithmic Fairness in Business Analytics: Directions for Research and Practicecited by 18
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  8. 2022Stochastic Differentially Private and Fair Learningcited by 16
  9. 2024From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditingcited by 15
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  11. 2022A study on the distribution of social biases in self-supervised learning visual modelscited by 13
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  13. 2022Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairnesscited by 13
  14. 2022The Conflict Between Explainable and Accountable Decision-Making Algorithmscited by 13
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  16. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  17. 2021Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectivenesscited by 12
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  28. 2021End-To-End Bias Mitigation: Removing Gender Bias in Deep Learningcited by 6
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  31. 2023Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparitycited by 5
  32. 2024Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERTcited by 5
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  36. 2022The Fairness Field Guide: Perspectives from Social and Formal Sciencescited by 3
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