GFLC: Graph-based Fairness-aware Label Correction for Fair Classification

TL;DR

This paper introduces GFLC, a graph-based method that corrects biased labels in training data to improve fairness and performance of machine learning classifiers, especially in sensitive applications.

Contribution

GFLC is a novel approach combining confidence measures, Ricci-flow graph regularization, and demographic parity incentives for label correction in fair ML.

Findings

Significant fairness improvements over baseline methods

Enhanced model performance with fairness trade-offs

Effective correction of biased labels in noisy datasets

Abstract

Fairness in machine learning (ML) has a critical importance for building trustworthy machine learning system as artificial intelligence (AI) systems increasingly impact various aspects of society, including healthcare decisions and legal judgments. Moreover, numerous studies demonstrate evidence of unfair outcomes in ML and the need for more robust fairness-aware methods. However, the data we use to train and develop debiasing techniques often contains biased and noisy labels. As a result, the label bias in the training data affects model performance and misrepresents the fairness of classifiers during testing. To tackle this problem, our paper presents Graph-based Fairness-aware Label Correction (GFLC), an efficient method for correcting label noise while preserving demographic parity in datasets. In particular, our approach combines three key components: prediction confidence measure, graph-based regularization through Ricci-flow-optimized graph Laplacians, and explicit demographic parity incentives. Our experimental findings show the effectiveness of our proposed approach and show significant improvements in the trade-off between performance and fairness metrics compared to the baseline.