FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations

Ioannis Sarridis, Christos Koutlis, Symeon Papadopoulos, Christos Diou

Keywords: Vision and Learning

Abstract: Bias in computer vision systems can perpetuate or even amplify discrimination against certain populations. Considering that bias is often introduced by biased visual datasets, many recent research efforts focus on training fair models using such data. However, most of them heavily rely on the availability of protected attribute labels in the dataset, which limits their applicability, while label-unaware approaches, i.e., approaches operating without such labels, exhibit considerably lower performance. To overcome these limitations, this work introduces FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels. To do that, FLAC proposes a sampling strategy that highlights underrepresented samples in the dataset, and casts the problem of learning fair representations as a probability matching problem that leverages representations extracted by a bias-capturing classifier. It is theoretically shown that FLAC can indeed lead to fair representations, that are independent of the protected attributes. This research has been published in IEEE Transactions on Pattern Analysis and Machine Intelligence journal (vol. 47, no. 2, pp. 1148-1160, Feb. 2025) and can be accessed via https://ieeexplore.ieee.org/abstract/document/10737139.

Submission Number: 60