Addressing Fairness and Explainability in Image Classification Using Optimal Transport

Our new paper, with François Hu and Philipp Ratz, Addressing Fairness and Explainability in Image Classification Using Optimal Transport, is now available on ArXiv.

Algorithmic Fairness and the explainability of potentially unfair outcomes are crucial for establishing trust and accountability of Artificial Intelligence systems in domains such as healthcare and policing. Though significant advances have been made in each of the fields separately, achieving explainability in fairness applications remains challenging, particularly so in domains where deep neural networks are used. At the same time, ethical data-mining has become ever more relevant, as it has been shown countless times that fairness-unaware algorithms result in biased outcomes. Current approaches focus on mitigating biases in the outcomes of the model, but few attempts have been made to try to explain why a model is biased. To bridge this gap, we propose a comprehensive approach that leverages optimal transport theory to uncover the causes and implications of biased regions in images, which easily extends to tabular data as well. Through the use of Wasserstein barycenters, we obtain scores that are independent of a sensitive variable but keep their marginal orderings. This step ensures predictive accuracy but also helps us to recover the regions most associated with the generation of the biases. Our findings hold significant implications for the development of trustworthy and unbiased AI systems, fostering transparency, accountability, and fairness in critical decision-making scenarios across diverse domains.

Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory

Our paper, Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory, writen with Denys Pommeret and Sam Stocksieker, is now available ArXiv.

In supervised learning, it is quite frequent to be confronted with real imbalanced datasets. This situation leads to a learning difficulty for standard algorithms. Research and solutions in imbalanced learning have mainly focused on classification tasks. Despite its importance, very few solutions exist for imbalanced regression. In this paper, we propose a data augmentation procedure, the GOLIATH algorithm, based on kernel density estimates which can be used in classification and regression. This general approach encompasses two large families of synthetic oversampling: those based on perturbations, such as Gaussian Noise, and those based on interpolations, such as SMOTE. It also provides an explicit form of these machine learning algorithms and an expression of their conditional densities, in particular for SMOTE. New synthetic data generators are deduced. We apply GOLIATH in imbalanced regression combining such generator procedures with a wild-bootstrap resampling technique for the target values. We evaluate the performance of the GOLIATH algorithm in imbalanced regression situations. We empirically evaluate and compare our approach and demonstrate significant improvement over existing state-of-the-art techniques.