Discrimination in Online Ad Delivery
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Abstract (arXiv)
A Google search for a person's name, such as "Trevon Jones", may yield a personalized ad for public records about Trevon that may be neutral, such as "Looking for Trevon Jones?", or may be suggestive of an arrest record, such as "Trevon Jones, Arrested?". This writing investigates the delivery of these kinds of ads by Google AdSense using a sample of racially associated names and finds statistically significant discrimination in ad delivery based on searches of 2184 racially associated personal names across two websites. First names, assigned at birth to more black or white babies, are found predictive of race (88% black, 96% white), and those assigned primarily to black babies, such as DeShawn, Darnell and Jermaine, generated ads suggestive of an arrest in 81 to 86 percent of name searches on one website and 92 to 95 percent on the other, while those assigned at birth primarily to whites, such as Geoffrey, Jill and Emma, generated more neutral copy: the word "arrest" appeared in 23 to 29 percent of name searches on one site and 0 to 60 percent on the other. On the more ad trafficked website, a black-identifying name was 25% more likely to get an ad suggestive of an arrest record. A few names did not follow these patterns. All ads return results for actual individuals and ads appear regardless of whether the name has an arrest record in the company's database. The company maintains Google received the same ad text for groups of last names (not first names), raising questions as to whether Google's technology exposes racial bias.
Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2013.
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Citing papers per year: 2021: 16; 2022: 23; 2023: 20; 2024: 23; 2025: 9; 2026: 2.
93 papers.
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- 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 117
- 2021A Survey on Bias in Visual Datasetscited by 84
- 2022Fairness in Graph Mining: A Surveycited by 76
- 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
- 2024On the Societal Impact of Open Foundation Modelscited by 64
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- 2021Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responsescited by 32
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- 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 29
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12 most cited of 93 All 93 (JSON)
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- 2025FnRGNN: Distribution-aware Fairness in Graph Neural Network22 Oct 2025
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- 2025Bias vs Bias -- Dawn of Justice: A Fair Fight in Recommendation Systems23 Jun 2025
- 2025How do datasets, developers, and models affect biases in a low-resourced language?: The Case of the Bengali Language7 Jun 2025
- 2025Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models4 Jun 2025
- 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems26 May 2025
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