Fairness Explainability using Optimal Transport with Applications in Image Classification

A revised version of our paper “Fairness Explainability using Optimal Transport with Applications in Image Classification” is now online, with more discussion about conterfactuals Ensuring trust and accountability in Artificial Intelligence systems demands explainability of its outcomes. Despite significant progress in Explainable AI, human biases still taint a substantial portion of its training data, raising concerns about unfairness or discriminatory tendencies. Current approaches in the field of Algorithmic Fairness focus on mitigating such biases in the outcomes of a model, …