Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendations
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Abstract (arXiv)
Bias mitigation methods for binary classification decision-making systems have been widely researched due to the ever-growing importance of designing fair machine learning processes that are impartial and do not discriminate against individuals or groups based on protected personal characteristics. In this paper, we present a structured overview of the research landscape for bias mitigation methods, report on their benefits and limitations, and provide recommendations for the development of future bias mitigation methods for binary classification.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 35 of its 82 references that are in the map. First posted on arXiv 31 May 2023.
Builds on
35 of 82 references.
References · 35
- 2016Equality of Opportunity in Supervised Learningcited by 2,007
- 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
- 2014Certifying and removing disparate impactcited by 715
- 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
- 2018A Reductions Approach to Fair Classificationcited by 587
- 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
- 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
- 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
- 2018Fair Clustering Through Fairletscited by 176
- 2021A survey on datasets for fairness-aware machine learningcited by 157
- 2017A Convex Framework for Fair Regressioncited by 148
- 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
23 more
- 2019Fair Algorithms for Clusteringcited by 98
- 2019Scalable Fair Clusteringcited by 78
- 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
- 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
- 2020Learning Certified Individually Fair Representationscited by 69
- 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifiercited by 63
- 2015On the relation between accuracy and fairness in binary classificationcited by 48
- 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
- 2020Fair clustering via equitable group representationscited by 43
- 2020Intra-Processing Methods for Debiasing Neural Networkscited by 27
- 2017Penalizing Unfairness in Binary Classificationcited by 25
- 2022Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approachescited by 25
- 2018Taking Advantage of Multitask Learning for Fair Classificationcited by 24
- 2020Making Fair ML Software using Trustworthy Explanationcited by 24
- 2019Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Datacited by 23
- 2019Fair Adversarial Gradient Tree Boostingcited by 17
- 2022Beyond Adult and COMPAS: Fairness in Multi-Class Predictioncited by 16
- 2019Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patternscited by 14
- 2022Blackbox Post-Processing for Multiclass Fairnesscited by 14
- 2019FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventionscited by 13
- 2020FairNN- Conjoint Learning of Fair Representations for Fair Decisionscited by 11
- 2020Fair Outlier Detectioncited by 10
- 2021Learning to Generate Fair Clusters from Demonstrationscited by 4
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