Title: Fairness-aware Classification: Criterion, Convexity, and Bounds,
Abstract: 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.