Fairness-aware Classification: Criterion, Convexity, and Bounds

By Yongkai Wu, Lu Zhang, Xintao Wu. 2018

28 citations recorded by the source corpus.

Fairness-aware classification is receiving increasing attention in the machine learning fields. Recently research proposes to formulate the fairness-aware classification as constrained optimization problems. However, several limitations exist in previous works due to the lack of a theoretical framework for guiding the formulation. In this paper, we propose a general framework for learning fair classifiers which addresses previous limitations. The framework formulates various commonly-used fairness metrics as convex constraints that can be directly incorporated into classic classification models. Within the framework, we propose a constraint-free criterion on the training data which ensures that any classifier learned from the data is fair. We also derive the constraints which ensure that the real fairness metric is satisfied when surrogate functions are used to achieve convexity. Our framework can be used to for formulating fairness-aware classification with fairness guarantee and computational efficiency. The experiments using real-world datasets demonstrate our theoretical results and show the effectiveness of proposed framework and methods.

Subfields: AI Ethics, Fairness & Society

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Citations in the corpus

A Survey on Bias and Fairness in Machine Learning (2019, methodology): “table toward change in the test set [59]. Other works tried to propose three different modifications to the existing Naive Bayes classifier for discrimination-free classification [24]. The authors in [128], propose a general framework for learning fair classifiers. This framework can be used for for”
Statistical Equity: A Fairness Classification Objective (2020, methodology): “Some other methods perform in-processing which tries to incorporate the fairness objective during training phase [Kamishima et al., 2012, Zafar et al., 2015, Wu, Zhang, and Wu, 2018].”