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Fairness-Aware Unsupervised Feature Selection

Published: 30 October 2021 Publication History

Abstract

Feature selection is a prevalent data preprocessing paradigm for various learning tasks. Due to the expensive cost of acquiring supervision information, unsupervised feature selection sparks great interests recently. However, existing unsupervised feature selection algorithms do not have fairness considerations and suffer from a high risk of amplifying discrimination by selecting features that are over associated with protected attributes such as gender, race, and ethnicity. In this paper, we make an initial investigation of the fairness-aware unsupervised feature selection problem and develop a principled framework, which leverages kernel alignment to find a subset of high-quality features that can best preserve the information in the original feature space while being minimally correlated with protected attributes. Specifically, different from the mainstream in-processing debiasing methods, our proposed framework can be regarded as a model-agnostic debiasing strategy that eliminates biases and discrimination before downstream learning algorithms are involved. Experimental results on real-world datasets demonstrate that our framework achieves a good trade-off between feature utility and promoting feature fairness.

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Cited By

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  • (2024)Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group FairnessProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679802(560-569)Online publication date: 21-Oct-2024
  • (2023)Fairness in Graph Mining: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326559835:10(10583-10602)Online publication date: 1-Oct-2023
  • (2023)Optimizing Equity: Working towards Fair Machine Learning Algorithms in Laboratory MedicineThe Journal of Applied Laboratory Medicine10.1093/jalm/jfac0858:1(113-128)Online publication date: 4-Jan-2023
  • Show More Cited By

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cover image ACM Conferences
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 30 October 2021

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Author Tags

  1. fairness
  2. feature selection
  3. unsupervised learning

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Cited By

View all
  • (2024)Integrating Fair Representation Learning with Fairness Regularization for Intersectional Group FairnessProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679802(560-569)Online publication date: 21-Oct-2024
  • (2023)Fairness in Graph Mining: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.326559835:10(10583-10602)Online publication date: 1-Oct-2023
  • (2023)Optimizing Equity: Working towards Fair Machine Learning Algorithms in Laboratory MedicineThe Journal of Applied Laboratory Medicine10.1093/jalm/jfac0858:1(113-128)Online publication date: 4-Jan-2023
  • (2022)Fairness-aware genetic-algorithm-based few-shot classificationMathematical Biosciences and Engineering10.3934/mbe.202316920:2(3624-3637)Online publication date: 2022

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