Towards Algorithmic Fairness by means of Instance-level Data Re-weighting based on Shapley Values
It addresses fairness in machine learning for societal applications, offering an interpretable and model-agnostic approach, though it is incremental as it builds on existing pre-processing methods.
The paper tackles algorithmic fairness by proposing FairShap, an instance-level data re-weighting method using Shapley Values to measure contributions to fairness metrics, resulting in fairer models with similar accuracy compared to baselines across various datasets and models.
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. Moreover, we perform a utility-fairness study and analyze FairShap's computational cost depending on the size of the dataset and the number of features. We believe that FairShap represents a novel contribution in interpretable and model-agnostic approaches to algorithmic fairness that yields competitive accuracy even when only biased training datasets are available.