A Review on Fairness in Machine Learning
Abstract
An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence and machine learning (ML) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans, and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop ML algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision making may be inherently prone to unfairness, even when there is no intention for it. This article presents an overview of the main concepts of identifying, measuring, and improving algorithmic fairness when using ML algorithms, focusing primarily on classification tasks. The article begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process, and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, toward a better understanding of which mechanisms should be used in different scenarios. The article ends by reviewing several emerging research sub-fields of algorithmic fairness, beyond classification.
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Numerous strategies exist to address bias and fairness in machine learning models.
“Numerous strategies exist to address bias and fairness in machine learning models 92 .”
From Machine culture (2023) · cites this paper as [92]
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Disparate Impact under Title VII: An Objective Theory of Discrimination
Rutherglen, G. A. (1987) · Virginia Law Review · doi:10.2307/1072940 · Cited by 44 · see citing works
Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations
Patro, G. K., Chakraborty, A., Ganguly, N., & Gummadi, K. P. (2020) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v34i01.5349 · Cited by 42 · see citing works
Fairness-Aware Loan Recommendation for Microfinance Services
Lee, E. L., Lou, J.-K., Chen, W.-M., Chen, Y.-C., Lin, S.-D., Chiang, Y.-S., & Chen, K.-T. (2014) · International Conference on Social Computing · doi:10.1145/2639968.2640064 · Cited by 41 · see citing works
Srivastava, M., Heidari, H., & Krause, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04783 · Cited by 41 · see citing works
Personalizing Fairness-aware Re-rankingPreprint
Liu, W., & Burke, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.02921 · Cited by 40 · see citing works
Eliciting and Enforcing Subjective Individual Fairness.Preprint
Jung, C., Kearns, M. J., Neel, S., Roth, A., Stapleton, L., & Wu, Z. S. (2019) · arXiv (Cornell University) · Cited by 37 · see citing works
Survey on Causal-based Machine Learning Fairness NotionsPreprint
Makhlouf, K., Zhioua, S., & Catuscia Palamidessi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09553 · Cited by 37 · see citing works
Fair Learning with Private Demographic DataPreprint
Mozannar, H., Ohannessian, M. I., & Srebro, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.11651 · Cited by 36 · see citing works
Stock, P., & Cissé, M. (2017) · Cited by 31 · see citing works
Learning Fair Classifiers: A Regularization-Inspired Approach.
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Improved Adversarial Learning for Fair ClassificationPreprint
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Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
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Balanced Neighborhoods for Fairness-Aware Collaborative Recommendation
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Generative Adversarial Networks for Mitigating Biases in Machine Learning Systems
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Privacy-Preserving Technologies
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New Fairness Metrics for Recommendation that Embrace DifferencesPreprint
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A Distributed Fair Machine Learning Framework with Private Demographic Data Protection
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Learning using privileged information
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The Price of Local Fairness in Multistage Selection
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Inferential Privacy Guarantees for Differentially Private MechanismsPreprint
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Path-Specific Counterfactual Fairness
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The Emerging Uniform Structure of Disparate Treatment Discrimination LitigationPreprint
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Controlling the Generalization Ability of Learning Processes
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Fairness and Missing ValuesPreprint
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Fairness-Driven Private Collaborative Machine Learning
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Self-citationIntegrated Public Use Microdata Series, International
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Does mitigating ML's disparate impact require disparate treatment?Preprint
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Ricci v DeStefano: Affirmative Action and the Lessons of Adversity
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Benchmarking Four Approaches to Fairness-Aware Machine Learning
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A Comparison of Fairness-Aware Machine Learning Algorithms
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Data Transparency and Fairness Analysis of the NYPD Stop-and-Frisk Program
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Ranking for Individual and Group Fairness Simultaneously.Preprint
Gorantla, S., Deshpande, A., & Louis, A. (2020) · arXiv (Cornell University) · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3643540 · Cited by 278 · see citing works
Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01742-2 · Cited by 132 · see citing works
The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective
Franklin, G., Stephens, R., Piracha, M., Tiosano, S., Lehouillier, F., Koppel, R., & Elkin, P. (2024) · Life · doi:10.3390/life14060652 · Cited by 76 · see citing works
Trust, trustworthiness and AI governance
Lahusen, C., Maggetti, M., & Slavkovik, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-71761-0 · Cited by 68 · see citing works
Fairness-Aware Machine Learning Engineering: How Far Are We?
Ferrara, C., Sellitto, G., Ferrucci, F., Palomba, F., & De Lucia, A. (2024) · Empirical Software Engineering · doi:10.1007/s10664-023-10402-y · Cited by 60 · see citing works
“A common practice in the literature is to compare different, often incompatible definitions and metrics of fairness, which, depending on the case, improve or worsen fairness levels, also impacting the performance and accuracy of the system (Pessach and Shmueli 2022).”
BackgroundGenerative Artificial Intelligence for Software Engineering—A Research Agenda
Nguyen‐Duc, A., Cabrero‐Daniel, B., Przybylek, A., Arora, C., Khanna, D., Herda, T., Rafiq, U., Melegati, J., Guerra, E., Kemell, K., et al. (2025) · Software: Practice and Experience · doi:10.1002/spe.70005 · Cited by 60 · see citing works
Sáez, C., Ferri, P., & García-Gómez, J. M. (2024) · Journal of Medical Internet Research · doi:10.2196/50295 · Cited by 36 · see citing works
Richet, J.-L. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100835 · Cited by 20 · see citing works
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems
Pagan, N., Baumann, J., Elokda, E., De Pasquale, G., Bolognani, S., & Hannák, A. (2023) · arXiv · doi:10.48550/arxiv.2305.06055 · Cited by 4 · see citing works
Constantin, R., Duck, M., Alexandrov, A., Matosevic, P., Keidar, D., & El-Assady, M. (2022) · 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX) · doi:10.1109/trex57753.2022.00005 · Cited by 3 · see citing works
- Authors
- Pessach, D., & Shmueli, E.
- Affiliations
- Tel Aviv University (IL)
- Venue
- ACM Computing Surveys
- Published
- 2022-02-03
- Volume
- 55
- Issue
- 3
- Pages
- 1-44
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4210736086
- Cited by
- 598
- References
- 138
- Citations by year
- 2026: 114 · 2025: 198 · 2024: 165 · 2023: 96 · 2022: 25
- FWCI
- 140.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Computing Surveys (publishedVersion)
- Funders
- Koret foundation grant for Smart Cities and Digital Living (2030)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- algorithmic fairness, algorithmic bias, fairness metrics, data preprocessing, in-processing, post-processing, classification, automated decision-making, machine learning
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.
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