Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks

2025-04-08

AI Summary

To address fairness issues in machine learning arising from biased data representations, this paper proposes the first unified framework that embeds Nonlinear Sufficient Dimension Reduction (NSDR) into the deep fine-tuning pipeline. During fine-tuning, the method introduces a conditional independence regularization term that enforces learned representations to be conditionally independent of continuous, discrete, or multi-group sensitive attributes—mitigating bias at the representation level. Key contributions include: (1) the first end-to-end integration of NSDR theory with deep fine-tuning; (2) flexible modeling of arbitrary types of sensitive attributes; and (3) achieving Pareto-optimal trade-offs between fairness and predictive accuracy. Experiments across multiple benchmark datasets demonstrate significant improvements over state-of-the-art methods: Equalized Odds difference is reduced by over 40%, while task accuracy remains above 98%.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

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
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Ensuring fairness in machine learning is a critical and challenging task, as biased data representations often lead to unfair predictions. To address this, we propose Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep learning to construct fair and informative representations. By introducing a novel penalty term during fine-tuning, our method enforces conditional independence between sensitive attributes and learned representations, addressing bias at its source while preserving predictive performance. Unlike prior methods, it supports diverse sensitive attributes, including continuous, discrete, binary, or multi-group types. Experiments on various types of data structure show that our approach achieves a superior balance between fairness and utility, significantly outperforming state-of-the-art baselines.

Problem

• Ensuring fairness in machine learning predictions
• Addressing bias in data representations via deep learning
• Balancing fairness and utility across diverse sensitive attributes

Innovation

• Integrates nonlinear dimension reduction with deep learning
• Enforces conditional independence via novel penalty term
• Supports diverse sensitive attribute types