Classifying without discriminating

Pattern classification and scene analysis

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Discrimination-aware data mining

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Interestingness of frequent itemsets using Bayesian networks as background knowledge

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Fast discovery of unexpected patterns in data, relative to a Bayesian network

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Knowledge-Based Sampling for Subgroup Discovery

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A Survey on Bias and Fairness in Machine Learning

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Explainable AI: a review of machine learning interpretability methods

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Certifying and Removing Disparate Impact

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Fairness and Abstraction in Sociotechnical Systems

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Data preprocessing techniques for classification without discrimination

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Bias in data‐driven artificial intelligence systems—An introductory survey

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Counterfactual Fairness

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Fairness in Criminal Justice Risk Assessments: The State of the Art

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Inherent trade-offs in the fair determination of risk scores

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Three naive Bayes approaches for discrimination-free classification.

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Algorithmic Fairness: Choices, Assumptions, and Definitions

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A comparative study of fairness-enhancing interventions in machine learning

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A Review on Fairness in Machine Learning

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Building Classifiers with Independency Constraints

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Fairness testing: testing software for discrimination

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FairGAN: Fairness-aware Generative Adversarial Networks

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Inherent Tradeoffs in the Fair Determination of Risk Scores

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603197 · Cited by 347 · see citing works

Discrimination Aware Decision Tree Learning

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Fairness in Machine Learning

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The (Im)possibility of fairness: different value systems require different mechanisms for fair decision making

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A survey on datasets for fairness‐aware machine learning

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Trustworthy ai: A computational perspective

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Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy

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On the (im)possibility of fairness

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Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information

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Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

Celis, L. E., Huang, L., Keswani, V., & Vishnoi, N. K. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287586 · Cited by 231 · see citing works

Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods

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Value-Sensitive Algorithm Design: Method, Case Study, and Lessons

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A clarification of the nuances in the fairness metrics landscape

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Human factors in model interpretability: Industry practices, challenges, and needs

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Fairness in credit scoring: Assessment, implementation and profit implications

Kozodoi, N., Jacob, J., & Lessmann, S. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.023 · Cited by 198 · see citing works

A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices

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How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis

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FairTest: Discovering Unwarranted Associations in Data-Driven Applications

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FairTest: Discovering Unwarranted Associations in Data-Driven Applications

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Algorithmic fairness in computational medicine

Xu, J., Xiao, Y., Wang, W. H., Ning, Y., Shenkman, E. A., Bian, J., & Wang, F. (2022) · eBioMedicine · doi:10.1016/j.ebiom.2022.104250 · Cited by 170 · see citing works

A Confidence-Based Approach for Balancing Fairness and Accuracy

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Empirical Risk Minimization under Fairness ConstraintsPreprint

Donini, M., Oneto, L., Ben-David, S., Shawe-Taylor, J., & Pontil, M. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1802.08626 · Cited by 522 · see citing works

Fairness is not static

D’Amour, A., Srinivasan, H., Atwood, J. R., Baljekar, P. N., Sculley, D., & Halpern, Y. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372878 · Cited by 156 · 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

Discrimination, artificial intelligence, and algorithmic decision-making

Borgesius, F. Z. (2025) · arXiv · doi:10.48550/arxiv.2510.13465 · Cited by 152 · see citing works

Identifying and Correcting Label Bias in Machine Learning

Jiang, H., & Nachum, O. (2019) · arXiv · doi:10.48550/arxiv.1901.04966 · Cited by 152 · see citing works

Interventional Fairness: Causal Database Repair for Algorithmic Fairness

Salimi, B., Rodriguez, L., Howe, B., & Suciu, D. (2019) · Proceedings of the 2019 International Conference on Management of Data · doi:10.1145/3299869.3319901 · Cited by 145 · see citing works

Preventing undesirable behavior of intelligent machines

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Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models

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An algorithm for removing sensitive information: Application to race-independent recidivism prediction

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A Moral Framework for Understanding Fair ML through Economic Models of Equality of Opportunity

Heidari, H., Loi, M., Gummadi, K. P., & Krause, A. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287584 · Cited by 141 · see citing works

Fairness in rankings and recommendations: an overview

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Software fairness

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Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making

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Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures

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Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification

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Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models

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Invariant Representations without Adversarial TrainingPreprint

Moyer, D., Gao, S., Brekelmans, R., Steeg, G. V., & Galstyan, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09458 · Cited by 121 · see citing works

Understanding and Mitigating Annotation Bias in Facial Expression Recognition

Chen, Y., & Joo, J. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01471 · Cited by 120 · see citing works

Gender-preserving Debiasing for Pre-trained Word Embeddings

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Policy advice and best practices on bias and fairness in AI

Álvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I. T., Lobo, P. R., et al. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09746-w · Cited by 128 · see citing works

Toward creating a fairer ranking in search engine results

Gao, R., & Shah, C. S. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102138 · Cited by 107 · see citing works

Algorithmic Fairness

Pessach, D., & Shmueli, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_37 · Cited by 177 · see citing works

Does Object Recognition Work for Everyone?Preprint

DeVries, T., Misra, I., Wang, C., & van der Maaten, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02659 · Cited by 132 · see citing works

Quantifying explainable discrimination and removing illegal discrimination in automated decision making

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Themis: automatically testing software for discrimination

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Quantifying the Impact of User Attentionon Fair Group Representation in Ranked Lists

Sapiezynski, P., Zeng, W., E Robertson, R., Mislove, A., & Wilson, C. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3317595 · Cited by 92 · see citing works

Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

Khademi, A., Lee, S., Foley, D., & Honavar, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313559 · Cited by 87 · 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

A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions

Saghiri, A. M., Vahidipour, S. M., Jabbarpour, M. R., Sookhak, M., & Forestiero, A. (2022) · Applied Sciences · doi:10.3390/app12084054 · Cited by 101 · see citing works

Fairness issues, current approaches, and challenges in machine learning models

Jui, T. D., & Rivas, P. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02083-2 · Cited by 91 · see citing works

Fairea: a model behaviour mutation approach to benchmarking bias mitigation methods

Hort, M., Zhang, J. M., Sarro, F., & Harman, M. (2021) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3468264.3468565 · Cited by 80 · see citing works

Classification with No Discrimination by Preferential Sampling

Kamiran, F., & Calders, T. T. (2010) · TU/e Research Portal · Cited by 78 · see citing works

Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems

Abdollahi, B., & Nasraoui, O. (2018) · Human-computer interaction series · doi:10.1007/978-3-319-90403-0_2 · Cited by 78 · see citing works

Inherent Tradeoffs in Learning Fair RepresentationsPreprint

Zhao, H., & Gordon, G. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.08386 · Cited by 82 · see citing works

Achieving Causal Fairness through Generative Adversarial Networks

Xu, D., Wu, Y., Yuan, S., Zhang, L., & Wu, X. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/201 · Cited by 76 · see citing works

Learning Fair Classifiers

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Non-Discriminatory Machine Learning through Convex Fairness Criteria

Goel, N., Yaghini, M., & Faltings, B. (2018) · Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278722 · Cited by 79 · see citing works

Wasserstein Fair ClassificationPreprint

Jiang, R., Pacchiano, A., Stepleton, T., Jiang, H., & Chiappa, S. (2019) · Conference on Uncertainty in Artificial Intelligence · doi:10.48550/arxiv.1907.12059 · Cited by 225 · see citing works

Fairness by Learning Orthogonal Disentangled Representations

Sarhan, M. H., Navab, N., Eslami, A., & Albarqouni, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58526-6_44 · Cited by 69 · see citing works

Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603203 · Cited by 67 · see citing works

A brief review on algorithmic fairness

Wang, X., Zhang, Y., & Zhu, R. (2022) · Management System Engineering · doi:10.1007/s44176-022-00006-z · Cited by 73 · see citing works

Taking Advantage of Multitask Learning for Fair Classification

Oneto, L., Doninini, M., Elders, A., & Pontil, M. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314255 · Cited by 64 · see citing works

The many dimensions of algorithmic fairness in educational applications

Loukina, A., Madnani, N., & Zechner, K. (2019) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/w19-4401 · Cited by 64 · see citing works

Fair regression for health care spending

Zink, A., & Rose, S. (2019) · Biometrics · doi:10.1111/biom.13206 · Cited by 57 · see citing works

Integrating induction and deduction for finding evidence of discrimination

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Combating discrimination using Bayesian networks

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Fair Forests: Regularized Tree Induction to Minimize Model Bias

Raff, E., Sylvester, J., & Mills, S. (2017) · AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3278721.3278742 · Cited by 79 · see citing works

Managing Bias in Machine Learning Projects

Fahse, T., Huber, V., & van Giffen, B. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86797-3_7 · Cited by 54 · see citing works

D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias

Ghai, B., & Mueller, K. D. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209484 · Cited by 68 · see citing works

Mitigating Bias in Algorithmic Systems—A Fish-eye View

Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2022) · ACM Computing Surveys · doi:10.1145/3527152 · Cited by 54 · see citing works

Fair Kernel Learning

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Fairness-Aware Programming

Albarghouthi, A., & Vinitsky, S. (2019) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3287560.3287588 · Cited by 52 · see citing works

Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision MakingPreprint

Heidari, H., Ferrari, C., Gummadi, K. P., & Krause, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04959 · Cited by 52 · see citing works

Accuracy and Fairness for Juvenile Justice Risk Assessments

Berk, R. (2019) · Journal of Empirical Legal Studies · doi:10.1111/jels.12206 · Cited by 50 · see citing works

Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness

Nakao, Y., Strappelli, L., Stumpf, S., Naseer, A., Regoli, D., & Gamba, G. D. (2022) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2022.2067936 · Cited by 55 · see citing works

Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

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FNNC: Achieving Fairness through Neural Networks

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Towards Unbiased and Accurate Deferral to Multiple Experts

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Building Better Open-Source Tools to Support Fairness in Automated Scoring

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On Fairness and Calibration

Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603207 · Cited by 49 · see citing works

Rule Protection for Indirect Discrimination Prevention in Data Mining

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On Discrimination Discovery and Removal in Ranked Data using Causal Graph

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