Skip to main content

Research Repository

Advanced Search

Learning Fair Representations through Uniformly Distributed Sensitive Attributes

Kenfack, P.; Rivera, A.; Khan, A.; Mazzara, M.

Authors

P. Kenfack

A. Rivera

M. Mazzara



Abstract

Machine Learning (ML) models trained on biased data can reproduce and even amplify these biases. Since such models are deployed to make decisions that can affect people's lives, ensuring their fairness is critical. One approach to mitigate possible unfairness of ML models is to map the input data into a less-biased new space by means of training the model on fair representations. Several methods based on adversarial learning have been proposed to learn fair representation by fooling an adversary in predicting the sensitive attribute (e.g., gender or race). However, adversarial-based learning can be too difficult to optimize in practice; besides, it penalizes the utility of the representation. Hence, in this research effort we train bias-free representations from the input data by inducing a uniform distribution over the sensitive attributes in the latent space. In particular, we propose a probabilistic framework that learns these representations by enforcing the correct reconstruction of the original data, plus the prediction of the attributes of interest while eliminating the possibility of predicting the sensitive ones. Our method leverages the inability of Deep Neural Networks (DNNs) to generalize when trained on a noisy label space to regularize the latent space. We use a network head that predicts a noisy version of the sensitive attributes in order to increase the uncertainty of their predictions at test time. Our experiments in two datasets demonstrated that the proposed model significantly improves fairness while maintaining the prediction accuracy of downstream tasks.

Citation

Kenfack, P., Rivera, A., Khan, A., & Mazzara, M. (2023, February). Learning Fair Representations through Uniformly Distributed Sensitive Attributes. Presented at 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), Raleigh, NC, USA

Presentation Conference Type Conference Paper (Published)
Conference Name 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
Start Date Feb 8, 2023
End Date Feb 10, 2023
Acceptance Date Dec 13, 2022
Online Publication Date Jun 1, 2023
Publication Date 2023
Deposit Date Dec 5, 2023
Publicly Available Date Jun 2, 2025
Publisher Institute of Electrical and Electronics Engineers
Pages 58-67
Book Title 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
ISBN 978-1-6654-6299-0
DOI https://doi.org/10.1109/SaTML54575.2023.00014
Keywords training; deep learning; uncertainty; neural networks; predictive models; probabilistic logic; data models
Public URL https://hull-repository.worktribe.com/output/4399851
Publisher URL https://ieeexplore.ieee.org/document/10136151