Fairness-aware Classification: Criterion, Convexity, and Bounds
This work addresses fairness in machine learning classification, providing a theoretical foundation for practitioners, though it is incremental in building upon existing constrained optimization methods.
The paper tackles the lack of a theoretical framework for fairness-aware classification by proposing a general framework that formulates fairness metrics as convex constraints, ensuring fairness guarantees and computational efficiency, with experiments on real-world datasets validating the approach.
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.