A Differentiable Distance Approximation for Fairer Image Classification

TL;DR

This paper introduces a differentiable approximation of demographic variance to measure and reduce bias in image classification models, enabling fairness improvements without extra computational overhead.

Contribution

It proposes a novel differentiable metric for demographic variance that can be optimized directly during training to enhance fairness.

Findings

Improves fairness in models across various datasets and tasks.

Maintains high classification accuracy while reducing bias.

Eliminates need for additional models during training.

Abstract

Naively trained AI models can be heavily biased. This can be particularly problematic when the biases involve legally or morally protected attributes such as ethnic background, age or gender. Existing solutions to this problem come at the cost of extra computation, unstable adversarial optimisation or have losses on the feature space structure that are disconnected from fairness measures and only loosely generalise to fairness. In this work we propose a differentiable approximation of the variance of demographics, a metric that can be used to measure the bias, or unfairness, in an AI model. Our approximation can be optimised alongside the regular training objective which eliminates the need for any extra models during training and directly improves the fairness of the regularised models. We demonstrate that our approach improves the fairness of AI models in varied task and dataset scenarios, whilst still maintaining a high level of classification accuracy. Code is available at https://bitbucket.org/nelliottrosa/base_fairness.