GFLC: Graph-based Fairness-aware Label Correction for Fair Classification
This addresses fairness issues in machine learning for applications like healthcare and legal judgments, but it is incremental as it builds on existing debiasing techniques.
The paper tackles the problem of biased and noisy labels in training data affecting fairness and performance in classification, presenting GFLC, a method that corrects label noise while preserving demographic parity, with experimental results showing significant improvements in the trade-off between performance and fairness metrics compared to baselines.
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.