Dataset Fairness: Achievable Fairness on Your Data With Utility Guarantees

arXiv:2402.17106v1 Announce Type: cross
Abstract: In machine learning fairness, training models which minimize disparity across different sensitive groups often leads to diminished accuracy, a phenomenon known as the fairness-accuracy trade-off. The severity of this trade-off fundamentally depends on dataset characteristics such as dataset imbalances or biases. Therefore using a uniform fairness requirement across datasets remains questionable and can often lead to models with substantially low utility. To address this, we present a computationally efficient approach to approximate the fairness-accuracy trade-off …

abstract accuracy arxiv biases cs.cy cs.lg data dataset fairness leads machine machine learning stat.ml trade trade-off training training models type uniform utility