Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Values
Keywords: Algorithmic Fairness, Data Valuation, Shapley Values, Re-weighting
TL;DR: FairShap is a novel method for fair algorithmic decision-making by re-weighting data, focusing on fair data valuation using Shapley Values.
Abstract: Algorithmic fairness is of utmost societal importance, yet state-of-the-art large-scale machine learning models require training with massive datasets that are frequently biased. In this context, pre-processing methods that focus on modeling and correcting bias in the data emerge as valuable approaches. In this paper, we propose *FairShap*, a novel instance-level data re-weighting method for fair algorithmic decision-making through data valuation by means of Shapley Values. *FairShap* is model-agnostic and easily interpretable. 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 a variety of training scenarios and machine learning models and show how it yields fairer models with similar levels of accuracy than the baselines. We illustrate *FairShap*'s interpretability by means of histograms and latent space visualizations and perform a utility-fairness study. We believe that *FairShap* represents a promising direction in interpretable and model-agnostic approaches to algorithmic fairness that yield competitive accuracy even when only biased datasets are available.
Primary Subject Area: Other
Paper Type: Research paper: up to 8 pages
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Submission Number: 36
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