Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks
This addresses fairness issues in machine learning for applications where biased data leads to unfair predictions, representing an incremental improvement over prior methods by supporting diverse sensitive attributes.
The paper tackled the problem of bias in machine learning by proposing Deep Fair Learning, a framework that integrates nonlinear sufficient dimension reduction with deep learning to enforce fairness in representations while preserving predictive performance, achieving a superior balance between fairness and utility and significantly outperforming state-of-the-art baselines in experiments.
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.