FairLabeler: Achieving Fairness via Biased Label Detection and Correction

Fairness in machine learning (ML)-based software has gained increasing attention as these systems are deployed in critical decision-making. Since ML software learns from labeled data, biased labels can substantially affect prediction outcomes, potentially resulting in discriminatory behavior. This study presents FairLabeler, a model-agnostic preprocessing approach that detects and massages biased labels in training datasets, thereby improving both individual-and group-level fairness while preserving predictive performance. We evaluate FairLabeler on nine real-world datasets using standard ML classifiers and a deep neural network. Experimental results show that FairLabeler surpasses the state-of-the-art bias mitigation method, as assessed with the fairness–performance tradeoff tool Fairea, achieving fairness with improved performance in 11% more cases. To facilitate replication and further research, we publicly release the code and datasets athttps://github.com/fairlabeler/anonymous.