A Differentiable Distance Approximation for Fairer Image Classification (with Nicholas Rosa and Mehrtash Harandi)

This paper shows how to train image classifiers that are more equitable across demographic or sensitive subgroups, by adding a differentiable approximation of the distance between per-group accuracy distributions to the training objective. Because the approximation is smooth and end-to-end differentiable, standard SGD can directly push the network toward equalised performance rather than just high average accuracy, without the discrete/bi-level optimisation that earlier fairness methods needed.

[ACCV 2022 paper]