Being able to decorrelate a feature space from protected attributes is an
area of active research and study in ethics, fairness, and also natural
sciences. We introduce a novel decorrelation method using Convex Neural Optimal
Transport Solvers (Cnots), that is able to decorrelate continuous feature space
against protected attributes with optimal transport. We demonstrate how well it
performs in the context of jet classification in high energy physics, where
classifier scores are desired to be decorrelated from the mass of a jet. The
decorrelation achieved in binary classification approaches the levels achieved
by the state-of-the-art using conditional normalising flows. When moving to
multiclass outputs the optimal transport approach performs significantly better
than the state-of-the-art, suggesting substantial gains at decorrelating
multidimensional feature spaces