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This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models.
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k-NN as an implementation of situation testing for discrimination discovery and prevention. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining . 502–510
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Data preprocessing techniques for classification without discrimination
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Decision theory for discrimination-aware classification. In 2012 IEEE 12th International Conference on Data Mining . IEEE, 924–929
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Fairness-aware classifier with prejudice remover regularizer. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 35–50
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Controlling attribute effect in linear regression. In 2013 IEEE 13th international conference on data mining . IEEE, 71–80
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The independence of fairness-aware classifiers. In 2013 IEEE 13th International Conference on Data Mining Workshops . IEEE, 849–858
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Discrimination aware classification for imbalanced datasets. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management . 1529–1532
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Fair boosting: a case study. In Workshop on Fairness, Accountability, and Transparency in Machine Learning . Citeseer
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Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences
Kazuto Fukuchi and Jun Sakuma. 2015 · 2015
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Discrimination-aware association rule mining for unbiased data analytics. In International Conference on Big Data Analytics and Knowledge Discovery . Springer, 108–120
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A confidence-based approach for balancing fairness and accuracy. In Proceedings of the 2016 SIAM International Conference on Data Mining . SIAM, 144–152
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Satisfying real-world goals with dataset constraints. In Advances in Neural Information Processing Systems . 2415–2423
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Impartial predictive modeling: Ensuring fairness in arbitrary models
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Fairness in learning: Classic and contextual bandits
Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth. 2016 · 2016
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Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard S. Zemel. 2016 · 2016
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A statistical framework for fair predictive algorithms
Kristian Lum and James Johndrow. 2016 · 2016
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A convex framework for fair regression
Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. 2017 · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi. 2017 · 2017
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Optimized pre-processing for discrimination prevention. In Advances in Neural Information Processing Systems . 3992–4001
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney. 2017 · 2017
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Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining . 797–806
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq. 2017 · 2017
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A continuous framework for fairness
Philipp Hacker and Emil Wiedemann. 2017 · 2017
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Avoiding Discrimination through Causal Reasoning. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS’17) . Curran Associates Inc., Red Hook, NY, USA, 656–666
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Two-stage algorithm for fairness-aware machine learning
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On fairness and calibration. In Advances in Neural Information Processing Systems . 5680–5689
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017 · 2017
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Recycling privileged learning and distribution matching for fairness
Novi Quadrianto and Viktoriia Sharmanska. 2017 · 2017
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When worlds collide: integrating different counterfactual assumptions in fairness. In Advances in neural information processing systems , Vol. 30. NIPS Proceedings
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Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 32
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Fair Selective Classification via Sufficiency. In International Conference on Machine Learning . PMLR, 6076–6086
Joshua K Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, and Gregory W Wornell. 2021 · 2021
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The landscape and gaps in open source fairness toolkits. In Proceedings of the 2021 CHI conference on human factors in computing systems . 1–13
Michelle Seng Ah Lee and Jat Singh. 2021 · 2021
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Yet Another Predictive Model? Fair Predictions of Students’ Learning Outcomes in an Online Math Learning Platform. In LAK21: 11th International Learning Analytics and Knowledge Conference . 572–578
Chenglu Li, Wanli Xing, and Walter Leite. 2021 · 2021
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Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
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Priority-based Post-Processing Bias Mitigation for Individual and Group Fairness
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Learning fair representations via an adversarial framework
Rui Feng, Yang Yang, Yuehan Lyu, Chenhao Tan, Yizhou Sun, and Chunping Wang. 2019 · 2019
Cited alongside, same era.
A comparative study of fairness-enhancing interventions in machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, 329–338
Sorelle A Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P Hamilton, and Derek Roth. 2019 · 2019
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Obtaining fairness using optimal transport theory. In International Conference on Machine Learning . PMLR, 2357–2365
Paula Gordaliza, Eustasio Del Barrio, Gamboa Fabrice, and Jean-Michel Loubes. 2019 · 2019
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