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 …