research-article

Towards Fair Unsupervised Learning

Authors:

Francois Buet-Golfouse,

Islam UtyagulovAuthors Info & Claims

FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Pages 1399 - 1409

https://doi.org/10.1145/3531146.3533197

Published: 20 June 2022 Publication History

Abstract

Bias-mitigating techniques are now well established in the supervised learning literature and have shown their ability to tackle fairness-accuracy, as well as fairness-fairness trade-offs. These are usually predicated on different conceptions of fairness, such as demographic parity or equal odds that depend on the available labels in the dataset. However, it is often the case in practice that unsupervised learning is used as part of a machine learning pipeline (for instance, to perform dimensionality reduction or representation learning via SVD) or as a standalone model (for example, to derive a customer segmentation via k-means). It is thus crucial to develop approaches towards fair unsupervised learning. This work investigates fair unsupervised learning within the broad framework of generalised low-rank models (GLRM). Importantly, we introduce the concept of fairness functional that encompasses both traditional unsupervised learning techniques and min-max algorithms (whereby one minimises the maximum group loss). To do so, we design straightforward alternate convex search or biconvex gradient descent algorithms that also provide partial debiasing techniques. Finally, we show on benchmark datasets that our fair generalised low-rank models (“fGLRM”) perform well and help reduce disparity amongst groups while only incurring small runtime overheads.

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Towards Fair Unsupervised Learning

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FAccT '22: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

June 2022

2351 pages

ISBN:9781450393522

DOI:10.1145/3531146

Copyright © 2022 ACM.

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Published: 20 June 2022

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FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency

June 21 - 24, 2022

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https://dl.acm.org/doi/10.1145/3632121
Newman POpdahl TLiu YWehrwein SElglaly YStephenson BStone JBattestilli LRebelsky SShoop L(2024)Crafting Disability Fairness Learning in Data Science: A Student-Centric Pedagogical ApproachProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630815(944-950)Online publication date: 7-Mar-2024
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Noghre GPazho ATabkhi H(2024)An Exploratory Study on Human-Centric Video Anomaly Detection through Variational Autoencoders and Trajectory Prediction2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)10.1109/WACVW60836.2024.00109(995-1004)Online publication date: 1-Jan-2024
https://doi.org/10.1109/WACVW60836.2024.00109
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