Fair Supervised Learning Through Constraints on Smooth Nonconvex Unfairness-Measure Surrogates

TL;DR

This paper introduces a novel smooth nonconvex surrogate for unfairness measures in supervised learning, enabling fair models through hard constraints without complex regularization, and supports multiple unfairness measures simultaneously.

Contribution

It proposes a new smoothing-based surrogate for unfairness, and a constraint-based strategy for fair supervised learning that is easier to optimize and tune than regularization methods.

Findings

The surrogate tightly approximates unfairness measures ensuring fairness.

The constraint-based approach simplifies optimization and tuning.

Supports multiple unfairness constraints simultaneously.

Abstract

A new strategy for fair supervised machine learning is proposed. The main advantages of the proposed strategy as compared to others in the literature are as follows. (a) We introduce a new smooth nonconvex surrogate to approximate the Heaviside functions involved in discontinuous unfairness measures. The surrogate is based on smoothing methods from the optimization literature, and is new for the fair supervised learning literature. The surrogate is a tight approximation which ensures the trained prediction models are fair, as opposed to other (e.g., convex) surrogates that can fail to lead to a fair prediction model in practice. (b) Rather than rely on regularizers (that lead to optimization problems that are difficult to solve) and corresponding regularization parameters (that can be expensive to tune), we propose a strategy that employs hard constraints so that specific tolerances for unfairness can be enforced without the complications associated with the use of regularization. (c) Our proposed strategy readily allows for constraints on multiple (potentially conflicting) unfairness measures at the same time. Multiple measures can be considered with a regularization approach, but at the cost of having even more difficult optimization problems to solve and further expense for tuning. By contrast, through hard constraints, our strategy leads to optimization models that can be solved tractably with minimal tuning.