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.
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“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
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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
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- 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).
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Cited by
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Items 601–700 of 754
ROBINSON, R. L. (2020) · medRxiv · doi:10.1101/2020.02.22.20026740
Diversifying Anonymized Data with Diversity ConstraintsPreprint
Milani, M., Huang, Y., & Chiang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09141
“Despite a strong need for algorithmic fairness and data diversity, such principles are rarely applied in practice [25].”
BackgroundFriedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0007
Financial Education for Liberation
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Friedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0006
Calibrating the Financial System
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Theorizing Moves to Financial Innocence
Friedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0004
Friedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0010
Financialization and the Tyranny of Bootstraps
Friedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0003
Rodríguez-Prieto, R. (2020) · Cuadernos Electrónicos de Filosofía del Derecho · doi:10.7203/cefd.43.17548
Corporate Landlords and the Climate Crisis
Friedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0005
Friedline, T. L. (2020) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.003.0011
Reber, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02018
“• Scoring credit risk (Citron & Pasquale, 2014), recidivism (Larson et al., 2016) and social behavior (Kühnreich, 2017; Stanley, 2015) • Nation-wide face recognition (Chen, 2017) and predictive policing (Peteranderl, 2017) • Fake News (Albright, 2017) and emotional manipulation in social networks (Kramer et al., 2014…”
BackgroundInfluentialSTEM and teens : an algorithm bias on a social media
Cecere, G., Jean, C., Guel, F. L., & Manant, M. (2017) · HAL
Do Algorithms Dream of 'Data' Without Bodies?Preprint
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CONTROVERSIES ON ALGORITHMIC HARMS: corporate discourses on coded discrimination
da Silveira, S. A., & da Silva, T. R. (2020) · Revista Observatório · doi:10.20873/uft.2447-4266.2020v6n4a1en
Béranger, J. (2018) · doi:10.1002/9781119508632.refs
On the privacy implications of targeted advertising platforms
Venkatadri, G. (2020) · doi:10.17760/d20394153
Is Fair Advertising Good for Platforms?Preprint
Yuan, D., Aseri, M., & Mukhopadhyay, T. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3913739
O’Riordan, K. (2022) · doi:10.1093/obo/9780199756841-0275
An interdisciplinary examination of partisanship in web search
Robertson, R. E. (2021) · Digital Repository Service (Northeastern University) · doi:10.17760/d20413988
Online Content and Platform Liability
Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0009 · Cited by 1
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Disinformation in the Digital Age
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Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0006 · Cited by 1
Technology and the Covid-19 Pandemic
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Introduction: Why You Should Read This Book
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Facts about the European Digital Economy
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(2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.002.0010
Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0001 · Cited by 1
Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0004
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Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0007
Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0010
(2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.002.0004
(2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.002.0012
Mariniello, M. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.003.0011
(2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198831471.002.0003
Mitigating Algorithmic Biases through Incentive-Based Rating Systems
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Uma proposta para impulsionar as pesquisas em governança de algoritmos de IA e dadosPreprint
Braga, J., Regateiro, F. S., Stiubiener, I., & Braga, J. C. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/xcpsd
Correlations between Sustainable Development Goals and Discriminations Algorítmicas Laborais
Fidalgo, L. B. B. (2023) · Seven Editora eBooks · doi:10.56238/tfisdwv1-029
Mortensen, J. L., Siegfredsen, N., & Bechmann, A. (2023) · SAGE Publications Ltd eBooks · doi:10.4135/9781529782783.n41
Jiang, L. (2023) · State of the Planet · doi:10.5194/sp-2023-6-ac1
Do, V., Corbett‐Davies, S., Jamal Atif, C., & Usunier, N. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/714
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Moeding, A. (2023) · doi:10.53291/qptu2775
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Jiang, L. (2023) · State of the Planet · doi:10.5194/sp-2023-6-ac4
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Castelnovo, A., Inverardi, N., Malandri, L., Mercorio, F., Mezzanzanica, M., & Seveso, A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_17
Chandra, M., Ganguly, D., Saha, T., & Ounis, I. (2023) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3583780.3615261
“…means that there is a smaller degree of control on how the task-specific data abstractions are constructed via the model parameters (e.g., for a feature-based model, it is possible to leave out a certain set of features to a model, e.g., the race of a person for recidivism prediction [2, 37]).”
BackgroundTowards a Better Understanding of the Computer Vision Research Community in Africa
Siam, M. (2023) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3617694.3623221
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BackgroundHow Not to Get Bored, or Some Thoughts on the Methodology of Law & TechnologyPreprint
Pałka, P., & Brożek, B. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4586879
Washington, A. L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197693025.003.0005
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(2023) · Oxford University Press eBooks · doi:10.1093/oso/9780192870070.002.0004
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Mendonça, R. F., Filgueiras, F. D. B., & Almeida, V. A. F. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780192870070.003.0001
Mendonça, R. F., Filgueiras, F. D. B., & Almeida, V. A. F. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780192870070.003.0007
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Lin, Z., & Zhu, S. (2023) · International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE) · doi:10.1109/cbase60015.2023.10439130
“For example, Amazon’s AI resume screening system discriminates against women, and meta AD recommendation algorithms are suspected of violating the Fair Housing Act (FHA)[12].”
BackgroundKuhn, K. M. (2024) · Palgrave studies in equity, diversity, inclusion, and indigenization in business · doi:10.1007/978-3-031-54941-0_12
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Friedline, T. L. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.002.0004
Friedline, T. L. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.002.0006
Friedline, T. L. (2021) · Oxford University Press eBooks · doi:10.1093/oso/9780190944131.002.0003
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Chaudhary, A. (2024) · Research Square · doi:10.21203/rs.3.rs-4775268/v1 · Cited by 3
RetractedTaddeo, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197745441.002.0008
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Taddeo, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197745441.003.0004
Taddeo, M. (2024) · Oxford University Press eBooks · doi:10.1093/oso/9780197745441.002.0003
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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
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