Fairness without Demographics through Learning Graph of Gradients

TL;DR

This paper introduces a novel fairness method that uses a graph of gradients to improve group fairness in machine learning without relying on demographic data, demonstrating robustness and effectiveness.

Contribution

It proposes a new graph-based approach leveraging model gradients for fairness without demographics, outperforming surrogate grouping methods in noisy settings.

Findings

Improves fairness significantly without reducing accuracy

Robust to noise compared to surrogate grouping methods

Effective in scenarios lacking demographic information

Abstract

Machine learning systems are notoriously prone to biased predictions about certain demographic groups, leading to algorithmic fairness issues. Due to privacy concerns and data quality problems, some demographic information may not be available in the training data and the complex interaction of different demographics can lead to a lot of unknown minority subpopulations, which all limit the applicability of group fairness. Many existing works on fairness without demographics assume the correlation between groups and features. However, we argue that the model gradients are also valuable for fairness without demographics. In this paper, we show that the correlation between gradients and groups can help identify and improve group fairness. With an adversarial weighting architecture, we construct a graph where samples with similar gradients are connected and learn the weights of different samples from it. Unlike the surrogate grouping methods that cluster groups from features and labels as proxy sensitive attribute, our method leverages the graph structure as a soft grouping mechanism, which is much more robust to noises. The results show that our method is robust to noise and can improve fairness significantly without decreasing the overall accuracy too much.