Null-sampling for Interpretable and Fair Representations

TL;DR

This paper introduces a method for learning invariant, interpretable representations in data to improve fairness, robustness, and transparency in machine learning models, especially under biased training conditions.

Contribution

It proposes a novel adversarial null-sampling approach to produce invariant data representations, enhancing interpretability and fairness in biased datasets.

Findings

Effective on image and tabular datasets

Produces human-examinable data domain representations

Improves fairness under biased training conditions

Abstract

We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness. Invariance implies a selectivity for high level, relevant correlations w.r.t. class label annotations, and a robustness to irrelevant correlations with protected characteristics such as race or gender. We introduce a non-trivial setup in which the training set exhibits a strong bias such that class label annotations are irrelevant and spurious correlations cannot be distinguished. To address this problem, we introduce an adversarially trained model with a null-sampling procedure to produce invariant representations in the data domain. To enable disentanglement, a partially-labelled representative set is used. By placing the representations into the data domain, the changes made by the model are easily examinable by human auditors. We show the effectiveness of our method on both image and tabular datasets: Coloured MNIST, the CelebA and the Adult dataset.