The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Discrimination in online ad delivery
Abstract
Google ads, black names and white names, racial discrimination, and click advertising.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The impact of this work is undeniable -leading to voluntary moratoriums [30], and several successful lawsuits [75,79]; nation-wide waves of regulatory action and public outcry [74]; congressional hearings [3]; and the recalls or radical redesigns of the deployed algorithms [12,64,65,88].”
From Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance (2022) · cites this paper as [79]
In the citing paper
“This is not to say that algorithmic profiling is not problematic.”
From Artificial intelligence in support of the circular economy: ethical considerations and a path forward (2024) · cites this paper as Sweeney 2013
Citations
Cites
Bertrand, M., & Mullainathan, S. (2004) · American Economic Review · doi:10.1257/0002828042002561 · Cited by 4,504
Pang, B., & Lee, L. (2004) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1218955.1218990 · Cited by 3,376
(2004) · Choice Reviews Online · doi:10.5860/choice.41-2555 · Cited by 1,030
The Causes and Consequences of Distinctively Black Names
Fryer, R. G., & Levitt, S. D. (2004) · The Quarterly Journal of Economics · doi:10.1162/0033553041502180 · Cited by 669
Sweeney, L. (2013) · Queue · doi:10.1145/2460276.2460278 · Cited by 477
Self-citationUniform Guidelines on Employee Selection Procedures
Scheer, T. J. (2007) · Routledge eBooks · doi:10.1081/e-epap2-407 · Cited by 403
Equal Employment Opportunity Commission
(2012) · CQ Press eBooks · doi:10.4135/9781452241067.n3 · Cited by 275
Harris, P. M., & Keller, K. S. (2004) · Journal of Contemporary Criminal Justice · doi:10.1177/1043986204271678 · Cited by 154
The Causes and Consequences of Distinctively Black Names
Fryer, R. G., & Levitt, S. D. (2003) · National Bureau of Economic Research · doi:10.3386/w9938 · Cited by 55
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2011) · arXiv · doi:10.48550/arxiv.1104.3913 · Cited by 55
Research works printed but not linked: 6
- Central Hudson Gas & Electric Corp , v. Public Service Commission of New York. Supreme Court of the United States, 447 U.S. 557 , 1980 . Central Hudson Gas & Electric Corp, v. Public Service Commission of New York. Supreme Court of the United States, 447 U.S. 557, 1980.
- Google . Google announces first quarter 2011 financial results ; http://investor.google.com/earnings/ 2011 /Q1_google_earnings.html. Google. Google announces first quarter 2011 financial results; http://investor.google.com/earnings/2011/Q1_google_earnings.html.
- Panel on Methods for Assessing Discrimination , National Research Council. Measuring racial discrimination . National Academy Press , Washington, DC , 2004 . Panel on Methods for Assessing Discrimination, National Research Council. Measuring racial discrimination. National Academy Press, Washington, DC, 2004.
- Sweeney , L. Discrimination in online ad delivery ( 2013 ). ( For de tails, see full technical report at http://ssrn.com/abstract=2208240. Data, including Web pages and ads, archived at http://foreverdata.org/onlineads). Sweeney, L. Discrimination in online ad delivery (2013). (For details, see full technical report at http://ssrn.com/abstract=2208240. Data, including Web pages and ads, archived at http://foreverdata.org/onlineads).
- U.S. Commission on Civil Rights. Racism in America and how to combat it . Washington, DC , 1970 . U.S. Commission on Civil Rights. Racism in America and how to combat it. Washington, DC, 1970.
- WebShot Command Line Server Edition. Version 1.9.1.1; http://www.websitescreenshots.com/. WebShot Command Line Server Edition. Version 1.9.1.1; http://www.websitescreenshots.com/.
Cited by
Sorted by citations
Items 701–754 of 754
Bias Unveiled: Enhancing Fairness in German Word Embeddings with Large Language Models
Saeid, Y., & Kopinski, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78014-1_23
Inteligência artificial: desafios éticos e futuros
Leonel, J. S., Leonel, C. F. S., Byk, J., & Furtado, S. D. C. (2024) · Revista Bioética · doi:10.1590/1983-803420243739pt · Cited by 7
Fairer Public Benefit in Copyright LawPreprint
Levendowski, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5080208
A framework for solving bias in graph-based recommender systems with a causal perspective
Yang, K., Li, G., Wang, L., & Xie, J. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06388-3
“The same applies to shopping recommendations [11], where these biased issues may diminish the effectiveness, reliability, and interpretability of recommendations, producing diverse unfair phenomena and impairing the interests of different groups.”
Networked Employment DiscriminationPreprint
Rosenblat, A., Kneese, T., & boyd, D. (2017) · doi:10.31219/osf.io/dp5aq_v1
Consumer Perception of Advertising in Diverse and Inclusive Markets
RAKESHYANAND, N. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem46045
Gatete, O. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.140400020 · Cited by 3
Herasymuk, D., Poliakov, A., & Fedorchenko, V. (2025) · Innovative technologies and scientific solutions for industries · doi:10.30837/2522-9818.2025.1.005 · Cited by 1
How Risky Are Open Frontier Models?
Krier, S., Law, H., & Klyman, K. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780198940272.013.0023
Ethical Considerations in AI-Enabled HR Practices
Nayak, A. P., & Vasanth, B. S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8669-9.ch003
Precision justice: An imaginary of data and justice
Boenig-Liptsin, M. (2025) · Social Studies of Science · doi:10.1177/03063127251342275
“Others have meticulously documented the individual and societal harms that are produced by algorithmic categorizations (Benjamin, 2019b; Eubanks, 2018; Noble, 2018; Sweeney, 2013).”
Briggt, M., & Kalorii, D. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5372582
Unblackboxing How Sociotemporalities Inform AI Accountability: The Case of Targeted Advertising
Hardcastle, F., Henne, K., Harb, J. I., Lee, A., Viaña, J. N. M., & Halford, S. (2025) · Social Science Computer Review · doi:10.1177/08944393251365275 · Cited by 1
“For example, in 2013, Latanya Sweeney, then chief technologist of the United States Federal Trade Commission, wrote that Google searches for Black-sounding names were more likely to display ads suggestive of arrest records (Sweeney, 2013).”
Bucher, T. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780190493028.002.0008
Disaggregating Digital Rights: The Civil Rights-Civil Liberties Disconnect byPreprint
Moy, L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5375895
Deconstructing AI Power: From Political Capital to Algorithmic ControlPreprint
Qatrani, O. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5385575
RACISMO E ALGORITMOS NAS ELEIÇÕES DE 2024:
de Oliveira, I. S., Brito, L. H. D. S., & Braga, S. D. P. (2025) · Suffragium - Revista do Tribunal Regional Eleitoral do Ceará · doi:10.53616/suffragium.v14i24.215
Kostuj, L., & Trittin‐Ulbrich, H. (2025) · Emerald Group Publishing Limited eBooks · doi:10.1108/s0733-558x20250000095006
Liu, Y., Cao, Q., Shen, H., Zhang, K., Wu, Y., & Cheng, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757414
“An example is that Sweeney [65] conducted an independent third-party fairness audit on Google AdSense, employing the correlation between names and race to construct query samples of different races.”
Dinçer, E. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2607-8.ch004
Milea, T. D., & Siminiuc, R. (2025) · RoEduNet Conference: Networking in Education and Research (RoEduNet) · doi:10.1109/roedunet68395.2025.11208498
“Several follow-up audits noted slight left-leaning biases in news results [29], [30], though others found mixed or negligible effects.”
Erle, L., Timm, L., Eimler, S. C., & Straßmann, C. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217700 · Cited by 1
231 The emerging impact of artificial intelligence
Miller, H. E., & Engemann, K. J. (2025) · De Gruyter eBooks · doi:10.1515/9783111595306-001
Humanisierung des Posthumanen in Ecuador
Albornoz, M. B. (2025) · Springer eBooks · doi:10.1007/978-3-031-82360-2_15
Exploiting the Shared Storage API
Nisenoff, A., Stefan, D., & Christin, N. (2025) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3719027.3744848
Ghatak, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2357-2.ch007
Inherent Tradeoffs in the Fair Determination of Risk Scores
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603206
The Externalities of Exploration and How Data Diversity Helps Exploitation
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603199
Algorithmic monoculture and social welfare
(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202
Selection Problems in the Presence of Implicit Bias
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603200
Fairness-aware Machine Learning
Kamishima, T. (2025) · Ouyou toukeigaku · doi:10.5023/jappstat.54.81
Causal Lens on Fairness Metrics: A Minimal-Assumption Audit for Algorithmic DiscriminationPreprint
Zhang, X., Xu, Y., Li, Z., & Chen, F. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5637951
Practical Strategies for Applying the Disparate Impact Remover at Inference Time
Danilevskyi, M., Tellez, F. P., & Vasić, J. (2026) · Conference on Human Centred Artificial Intelligence - Education and Practice (HCAIep) · doi:10.1145/3777490.3777497
“Nevertheless, AI systems often replicate or even amplify existing biases, a concern documented in domains such as criminal justice [2, 15], healthcare [21, 25], hiring decisions [7, 22], recommendation systems [20], targeted advertising [24], and environmental sciences [17].”
Damjan, M., & Strojan, L. (2026) · Balkan yearbook of European and international law · doi:10.1007/978-3-032-16296-0_1
Methoden und Daten zur Erforschung spezieller Organisationen
Kirchner, S., Hecht, C., & Ametowobla, D. (2026) · Springer eBooks · doi:10.1007/978-3-658-08580-3_36-1
Sarial‐Abi, G., & Giannetti, V. (2026) · British Journal of Management · doi:10.1111/1467-8551.70045
Caetano, S. P., & Lee, S.-W. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.113066
Cao, S., Zhang, D., Duan, X., Wang, D., Fan, B., Sun, J., Zuo, M., Qi, B., & Li, H. (2026) · Journal of Optical Communications and Networking · doi:10.1364/jocn.589613
“Currently, there is no established mechanism for collaborative data sharing, relevant regulations remain underdeveloped, and severe data barriers persist across different industries and application scenarios [15–17].”
Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai'i
Zhao, D., Cha, H., Ryan, M. J., Wang, A., Baker-Ramos, R., Helekahi-Kaiwi, E.-B., Diego, R., Hester, J. D., & Yang, D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790958 · Cited by 1
“Although AI au-dits are often conducted by researchers [22, 31, 42, 43, 128] or practitioners [96, 132, 148], end users can help identify harms from AI systems by leveraging their lived experiences [37, 140].”
Hossain, K. A. (2026) · Atmospheric and Climate Sciences · doi:10.4236/acs.2026.162024
Mishra, H., & Soundarajan, S. (2026) · SpringerBriefs in intelligent systems · doi:10.1007/978-3-032-24145-0_1
Unfairness in Information Retrieval
Mishra, H., & Soundarajan, S. (2026) · SpringerBriefs in intelligent systems · doi:10.1007/978-3-032-24145-0_2
Can Large Language Models Really Recognize Your Name?
Pham, D., Kairouz, P., Mireshghallah, F., Bagdasarian, E., Pham, C. M., & Houmansadr, A. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806747 · Cited by 7
“We use Wilcoxon Signed-Rank test to assess the statistical significance of the changes in audit scores due to BPI.”
A Throughput Model Framework for Dynamic Ethical Governance in AI-Driven Biometric Systems
Rodgers, W., Udo, G., & Gemoets, L. A. (2026) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2026.3707746
“Concernssurroundingprivacy,algorithmicbias,informed consent,transparency,andaccountabilityhavebecomecentral todebatesaboutresponsibleAIdeployment[6],[7],[8],[9], [10].”
Robo-Advisors as Product in the Insurance Sector: Considerations Regarding Enforceability
Skalski, S., & Tereszkiewicz, P. (2025) · Springer eBooks · doi:10.1007/978-3-032-19720-7_19
Meng, T., Li, X., Cao, J., & Zou, X. (2026) · Marketing Intelligence & Planning · doi:10.1108/mip-01-2025-0048
Bilici, M. V. (2026) · İstanbul Üniversitesi Sosyoloji Dergisi / İstanbul University Journal of Sociology · doi:10.26650/sj.2025.46.1.1172
Intersectionality and the Internet: Race, Class, Gender, Sexuality, and the Digital
Noble, S., Brock, A., Gray, K. L., & Roberts, S. (2026) · Annual Review of Sociology · doi:10.1146/annurev-soc-090325-023632
Evaluating the Impact of Situation Testing on SMOTE-Based Sampling Techniques for Bias Mitigation
Shahrezaei, M. H., McDaid, K., & Loughran, R. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-25809-0_22
WIP: Integrating Human-Centered AI Algorithm Design into an Undergraduate Biomedical Imaging Course
Ranger, B. J., & Hira, A. (2026) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--61025
Introducing Accountable Design
Urquhart, L. D. (2026) · Cambridge University Press eBooks · doi:10.1017/9781108985734.002
Reporting Non-Consensual Intimate Imagery: An Audit Study of Deepfakes
Qiwei, L., Zhang, S., Kasper, A. T., Eaton, A. A., Ashkinaze, J., Schoenebeck, S. Y., & Gilbert, É. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816990 · Cited by 11
“One of the earliest and most influential online audit studies found that names typically associated with Black Americans were 25% more likely to trigger ads suggesting an arrest record compared to names associated with White Americans [109].”
AI-Mediated Assessment in Global Higher Education Trust and Integrity Quality Assurance Challenges
Guliani, N., & Shukla, N. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8939-4.ch004
A Convex Framework for Fair Regression
Berk, R., Heidari, H., Jabbari, S., Joseph, M., Kearns, M., Morgenstern, J., Neel, S., & Roth, A. (2017) · arXiv · doi:10.48550/arxiv.1706.02409 · Cited by 198
“…decisions about individual citizens (including in domains such as credit, employment, education and criminal sentencing [3, 4, 26, 29]) has been accompanied by increased reports of instances in which the algorithms and models employed can be unfair or discriminatory in a variety of ways [2, 30].”
Background
Metadata
- Authors
- Sweeney, L.
- Affiliations
- Harvard University (US)
- Corresponding author
- Sweeney, Latanya
- ORCID
- Sweeney, Latanya
- Venue
- Communications of the ACM
- Published
- 2013-05
- Volume
- 56
- Issue
- 5
- Pages
- 44-54
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2095932468
- Cited by
- 783
- References
- 10
- Citations by year
- 2026: 30 · 2025: 53 · 2024: 61 · 2023: 109 · 2022: 78 · 2021: 84 · 2020: 91 · 2019: 82 · 2018: 69 · 2017: 53 · 2016: 37 · 2015: 18 · 2014: 16 · 2013: 2
- FWCI
- 63.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Communications of the ACM (publishedVersion)
- Funders
- Division of Computer and Network Systems (CNS-1237235)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- online advertising, racial discrimination, algorithmic discrimination, Google AdWords, algorithmic fairness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
