mlsanityData to 7 Oct 2026

Discrimination in Online Ad Delivery

arXiv 1301.6822 ↗First posted 29 Jan 2013cs.IR cs.CYcited by 93

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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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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 16; 2022: 23; 2023: 20; 2024: 23; 2025: 9; 2026: 2.

93 papers.

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  2. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 117
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  5. 2023A Comprehensive Survey on Deep Graph Representation Learningcited by 72
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  10. 2022Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practicecited by 30
  11. 2021Envisioning Communities: A Participatory Approach Towards AI for Social Goodcited by 29
  12. 2024The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiringcited by 29
38 more
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  6. 2021Problematic Machine Behavior: A Systematic Literature Review of Algorithm Auditscited by 20
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  12. 2022Investigations of Performance and Bias in Human-AI Teamwork in Hiringcited by 16
  13. 2022Stochastic Differentially Private and Fair Learningcited by 16
  14. 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biasescited by 16
  15. 2024From Transparency to Accountability and Back: A Discussion of Access and Evidence in AI Auditingcited by 15
  16. 2022Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systemscited by 14
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  19. 2023Preventing Discriminatory Decision-making in Evolving Data Streamscited by 12
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  21. 2021Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice modelscited by 10
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  25. 2023Causal Fairness for Outcome Controlcited by 8
  26. 2024A structured regression approach for evaluating model performance across intersectional subgroupscited by 8
  27. 2022Behind the Mask: Demographic bias in name detection for PII maskingcited by 7
  28. 2024Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil societycited by 7
  29. 2024'Since Lawyers are Males..': Examining Implicit Gender Bias in Hindi Language Generation by LLMscited by 7
  30. 2022Measuring and signing fairness as performance under multiple stakeholder distributionscited by 6
  31. 2023DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervisioncited by 6
  32. 2023BanditQ: Fair Bandits with Guaranteed Rewardscited by 6
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  35. 2021Censorship of Online Encyclopedias: Implications for NLP Modelscited by 5
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  38. 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5

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