FairShap: A Data Re-weighting Approach for Algorithmic Fairness based on Shapley Values

Authors: Arnaiz-Rodríguez, A. , Escolano, F., Oliver, N.

External link: https://arxiv.org/abs/2303.01928
Publication: arXiv:2303.01928, 2023
DOI: https://doi.org/10.48550/arXiv.2303.01928
PDF: Click here for the PDF paper

A later version of this work has been published in ICLR 2024 Workshop on Data-centric Machine Learning Research (DMLR).

In this paper, we propose FairShap, a novel and interpretable pre-processing (re-weighting) method for fair algorithmic decision-making through data valuation. FairShap is based on the Shapley Value, a well-known mathematical framework from game theory to achieve a fair allocation of resources. Our approach is easily interpretable, as it measures the contribution of each training data point to a predefined fairness metric. We empirically validate FairShap on several state-of-the-art datasets of different nature, with different training scenarios and models. The proposed approach outperforms other methods, yielding significantly fairer models with similar levels of accuracy. In addition, we illustrate FairShap’s interpretability by means of histograms and latent space visualizations. We believe this work represents a promising direction in interpretable, model-agnostic approaches to algorithmic fairness.