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

2025-05-21

AI Summary

Addressing key challenges in supervised learning—namely, the non-differentiability of discontinuous fairness metrics, conflicts among multiple fairness objectives, and hyperparameter sensitivity of regularization-based approaches—this paper proposes a hard-constraint fairness learning framework. Our method introduces a smooth, non-convex surrogate function that tightly approximates Heaviside-type unfairness measures, transforming non-differentiable constraints into differentiable approximations. It directly enforces hard constraints on multiple (potentially conflicting) fairness metrics, eliminating optimization difficulties and costly hyperparameter tuning inherent to regularization. We formulate a non-convex optimization problem with explicit fairness constraints and solve it efficiently using robust numerical solvers, ensuring convergence and stability. Experiments demonstrate that our approach significantly improves practical fairness guarantees while maintaining training stability, generalization performance, and computational efficiency—without requiring expensive hyperparameter search.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Machine Learning: Ethics, Bias, and Fairness
• Search and Optimization: Non-convex Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

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.

Problem

• Proposes smooth nonconvex surrogates for fair supervised learning
• Uses hard constraints to enforce unfairness tolerances effectively
• Allows multiple unfairness measures constraints simultaneously

Innovation

• Smooth nonconvex surrogate for unfairness measures
• Hard constraints replace regularization for fairness
• Multiple unfairness measures handled via constraints