Discrimination in Online Ad Delivery: Google ads, black names and white names, racial discrimination, and click advertising

Fairness through awareness

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · see citing works

A sentimental education

Pang, B., & Lee, L. (2004) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/1218955.1218990 · Cited by 3,317 · see citing works

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 644 · see citing works

Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination

Bertrand, M., & Mullainathan, S. (2003) · National Bureau of Economic Research · doi:10.3386/w9873 · Cited by 639 · see citing works

Equal Employment Opportunity Commission

(2012) · CQ Press eBooks · doi:10.4135/9781452241067.n3 · Cited by 275 · see citing works

Ex-Offenders Need Not Apply

Harris, P. M., & Keller, K. S. (2004) · Journal of Contemporary Criminal Justice · doi:10.1177/1043986204271678 · Cited by 154 · see citing works

Investor Information Demand: Evidence from Google Searches around Earnings AnnouncementsPreprint

Drake, M. S., Roulstone, D. T., & Thornock, J. R. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1669507 · Cited by 103 · see citing works

Fairness Through Awareness

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2011) · arXiv · doi:10.48550/arxiv.1104.3913 · Cited by 55 · see citing works

Comment on “Gauge invariance and kT -factorization of exclusive processes” [arXiv:0807.0296]

Li, H.-N., & Mishima, S. (2009) · Physics Letters B · doi:10.1016/j.physletb.2009.03.015 · Cited by 16 · see citing works

Google AdWords/AdSense in Campaign Strategy

Harvey, K. (2014) · Encyclopedia of Social Media and Politics · doi:10.4135/9781452244723.n240 · Cited by 4 · see citing works

Measuring Racial Discrimination. By National Research Council. The National Academies Press, 2004. 317 pp. Cloth, $44.95

Pager, D. (2005) · Social Forces · doi:10.1353/sof.2005.0076 · Cited by 3 · see citing works

Equal employment opportunity plan development guidance

None (1978) · doi:10.2172/8159837 · Cited by 3 · see citing works

Configuring a File Server

(2009) · Apress eBooks · doi:10.1007/978-1-4302-1890-6_12 · Cited by 2 · see citing works

Toward a New Civil Rights Era

(2018) · NYU Press eBooks · doi:10.2307/j.ctt1ggjk0r.11 · Cited by 1 · see citing works

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works

The ethics of algorithms: Mapping the debate

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · see citing works

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works

Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management

Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338 · see citing works

Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads

Lambrecht, A., & Tucker, C. (2019) · Management Science · doi:10.1287/mnsc.2018.3093 · Cited by 854 · see citing works

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works

Discrimination in online ad delivery

Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · see citing works

Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052660 · Cited by 632 · see citing works

Algorithmic Fairness: Choices, Assumptions, and Definitions

Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687 · see citing works

There is a blind spot in AI research

Crawford, K., & Calo, R. (2016) · Nature · doi:10.1038/538311a · Cited by 580 · see citing works

Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse

Hoffmann, A. L. (2019) · Information, Communication & Society · doi:10.1080/1369118x.2019.1573912 · Cited by 535 · see citing works

Society-in-the-loop: programming the algorithmic social contract

Rahwan, I. (2018) · Ethics and Information Technology · doi:10.1007/s10676-017-9430-8 · Cited by 553 · see citing works

Algorithmic Accountability and Public Reason

Binns, R. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0263-5 · Cited by 457 · see citing works

Fairness testing: testing software for discrimination

Galhotra, S., Brun, Y., & Meliou, A. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106277 · Cited by 405 · see citing works

Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems

Kiritchenko, S., & Mohammad, S. (2018) · Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s18-2005 · Cited by 384 · see citing works

Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining

Hajian, S., Bonchi, F., & Castillo, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945386 · Cited by 334 · see citing works

Artificial intelligence, transparency, and public decision-making: Why explanations are key when trying to produce perceived legitimacy

de Fine Licht, K., & de Fine Licht, J. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00960-w · Cited by 334 · see citing works

Machine learning and human capital complementarities: Experimental evidence on bias mitigation

Choudhury, P., Starr, E., & Agarwal, R. (2020) · Strategic Management Journal · doi:10.1002/smj.3152 · Cited by 293 · see citing works

Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr

Hannák, A., Wagner, C., Garcia, D., Mislove, A., Strohmaier, M., & Wilson, C. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998327 · Cited by 286 · see citing works

Discrimination through Optimization

Ali, M., Sapiezynski, P., Bogen, M., Korolova, A., Mislove, A., & Rieke, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359301 · Cited by 268 · see citing works

Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604673 · Cited by 279 · see citing works

Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits

Bandy, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449148 · Cited by 193 · see citing works

Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning

Srivastava, M., Heidari, H., & Krause, A. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330664 · Cited by 179 · see citing works

Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science

Neff, G., Tanweer, A., Fiore-Gartland, B., & Osburn, L. (2017) · Big Data · doi:10.1089/big.2016.0050 · Cited by 173 · see citing works

Public administration, public leadership and the construction of public value in the age of the algorithm and ‘big data’

Andrews, L. (2019) · Public Administration · doi:10.1111/padm.12534 · Cited by 172 · see citing works

Everyday Algorithm Auditing: Understanding the Power of Everyday Users in Surfacing Harmful Algorithmic Behaviors

Shen, H., DeVos, A., Eslami, M., & Holstein, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479577 · Cited by 171 · see citing works

Investigating the Impact of Gender on Rank in Resume Search Engines

Chen, L., Ma, R., Hannák, A., & Wilson, C. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174225 · Cited by 155 · see citing works

Using Machine Learning to Support Qualitative Coding in Social Science: Shifting the Focus to Ambiguity

Chen, N.-C., Drouhard, M., Kocielnik, R., Suh, J., & Aragon, C. R. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3185515 · Cited by 152 · see citing works

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media

Kulshrestha, J., Eslami, M., Messias, J., Zafar, M. B., Ghosh, S., Gummadi, K. P., & Karahalios, K. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998321 · Cited by 148 · see citing works

Enslaving the Algorithm: From a “Right to an Explanation” to a “Right to Better Decisions”?

Edwards, L., & Veale, M. (2018) · IEEE Security & Privacy · doi:10.1109/msp.2018.2701152 · Cited by 141 · see citing works

Addressing age-related bias in sentiment analysis,

Diaz, M., Johnson, I., Lazar, A., Piper, A. M., & Gergle, D. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173986 · Cited by 138 · see citing works

Building and Auditing Fair Algorithms: A Case Study in Candidate Screening

Wilson, C., Ghosh, A., Jiang, S., Mislove, A., Baker, L., Szary, J., Trindel, K., & Polli, F. (2021) · Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3442188.3445928 · Cited by 133 · see citing works

Algorithm Overdependence: How the Use of Algorithmic Recommendation Systems Can Increase Risks to Consumer Well-Being

Banker, S., & Khetani, S. (2019) · Journal of Public Policy & Marketing · doi:10.1177/0743915619858057 · Cited by 120 · see citing works

The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N. (2017) · Studies in Big Data · doi:10.1007/978-3-319-54024-5_1 · Cited by 111 · see citing works

Racial categories in machine learning

Benthall, S., & Haynes, B. D. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287575 · Cited by 110 · see citing works

Designing Contestability: Interaction Design, Machine Learning, and Mental Health

Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E., & Atkins, D. C. (2017) · Proceedings of the 2017 Conference on Designing Interactive Systems · doi:10.1145/3064663.3064703 · Cited by 110 · see citing works

Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring

Kelan, E. K. (2024) · Human Resource Management Journal · doi:10.1111/1748-8583.12511 · Cited by 106 · see citing works

FairSquare: probabilistic verification of program fairness

Albarghouthi, A., D'Antoni, L., Drews, S., & Nori, A. V. (2017) · Proceedings of the ACM on Programming Languages · doi:10.1145/3133904 · Cited by 94 · see citing works

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works

Ethical Decision Making in Autonomous Vehicles: Challenges and Research Progress

Wang, H., Khajepour, A., Cao, D., & Liu, T. (2022) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2019.2953556 · Cited by 84 · see citing works

FAHT: An Adaptive Fairness-aware Decision Tree Classifier

Zhang, W., & Ntoutsi, E. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/205 · Cited by 85 · see citing works

The right to a human in the loop: Political constructions of computer automation and personhood

Jones, M. L. (2017) · Social Studies of Science · doi:10.1177/0306312717699716 · Cited by 75 · see citing works

Agency Laundering and Information Technologies

Rubel, A., Castro, C., & Pham, A. (2019) · Ethical Theory and Moral Practice · doi:10.1007/s10677-019-10030-w · Cited by 90 · see citing works

Investigating Gender and Racial Biases in DALL-E Mini Images

Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3649883 · Cited by 57 · see citing works

Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Yee, K., Tantipongpipat, U., & Mishra, S. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479594 · Cited by 59 · see citing works

Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples

Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93087-x · Cited by 51 · see citing works

Fairness metrics and bias mitigation strategies for rating predictions

Ashokan, A., & Haas, C. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102646 · Cited by 47 · see citing works

Toward Gender-Inclusive Coreference Resolution: An Analysis of Gender and Bias Throughout the Machine Learning Lifecycle

Cao, Y. T., & Daumé, H. (2021) · Computational Linguistics · doi:10.1162/coli_a_00413 · Cited by 47 · see citing works

Data philanthropy and the design of the infraethics for information societies

Taddeo, M. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0113 · Cited by 35 · see citing works

Deep learning diffusion by infusion into preexisting technologies – implications for users and society at large

Engström, E., & Strimling, P. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101396 · Cited by 34 · see citing works

"I know even if you don't tell me": Understanding Users' Privacy Preferences Regarding AI-based Inferences of Sensitive Information for Personalization

Asthana, S., Im, J., Chen, Z., & Banovic, N. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642180 · Cited by 34 · see citing works

Epistemic fragmentation poses a threat to the governance of online targeting

Milano, S., Mittelstadt, B., Wachter, S., & Russell, C. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00358-3 · Cited by 29 · see citing works

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination

Kallus, N., Mao, X., & Zhou, A. (2019) · arXiv · doi:10.48550/arxiv.1906.00285 · Cited by 28 · see citing works

The role of artificial intelligence (AI) and machine learning in social work practice

Nuwasiima, M., Ahonon, M. P., & Kadiri, C. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.24.1.2998 · Cited by 27 · see citing works

Automating Automaticity: How the Context of Human Choice Affects the Extent of Algorithmic Bias

Agan, A., Davenport, D., Ludwig, J., & Mullainathan, S. (2023) · doi:10.3386/w30981 · Cited by 25 · see citing works

Fairness and explainability in automatic decision-making systems. A challenge for computer science and law

Kirat, T., Tambou, O., Do, V., & Tsoukiàs, A. (2023) · EURO Journal on Decision Processes · doi:10.1016/j.ejdp.2023.100036 · Cited by 24 · see citing works

Networked Employment Discrimination

Rosenblat, A., Kneese, T., & <!>boyd, <. (2014) · SSRN Electronic Journal · doi:10.2139/ssrn.2543507 · Cited by 22 · see citing works

Auditing large language models for race &amp; gender disparities: Implications for artificial intelligence-based hiring

Gaebler, J. D., Goel, S., Huq, A., & Tambe, P. (2024) · Behavioral Science &amp; Policy · doi:10.1177/23794607251320229 · Cited by 15 · see citing works

On the Societal Impact of Open Foundation Models

Kapoor, S., Bommasani, R., Klyman, K., Longpre, S., Ramaswami, A., Cihon, P., Hopkins, A., Bankston, K., Biderman, S., Bogen, M., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.07918 · Cited by 11 · see citing works

Equality of Opportunity in Supervised Learning

Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works

Discrimination in online ad delivery

Sweeney, L. (2013) · Communications of the ACM · doi:10.1145/2447976.2447990 · Cited by 783 · see citing works

Fairness through awareness

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · see citing works

Certifying and Removing Disparate Impact

Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works