Fair Streaming Feature Selection

• URL: http://arxiv.org/abs/2406.14401v1
• Date: Thu, 20 Jun 2024 15:22:44 GMT
• Title: Fair Streaming Feature Selection
• Authors: Zhangling Duan, Tianci Li, Xingyu Wu, Zhaolong Ling, Jingye Yang, Zhaohong Jia,
• Abstract summary: We propose FairSFS, a novel algorithm for fair streaming feature selection. We show that FairSFS not only maintains accuracy that is on par with leading streaming feature selection methods but also significantly improves fairness metrics.
• Score: 9.327911386140109
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming feature selection techniques have become essential in processing real-time data streams, as they facilitate the identification of the most relevant attributes from continuously updating information. Despite their performance, current algorithms to streaming feature selection frequently fall short in managing biases and avoiding discrimination that could be perpetuated by sensitive attributes, potentially leading to unfair outcomes in the resulting models. To address this issue, we propose FairSFS, a novel algorithm for Fair Streaming Feature Selection, to uphold fairness in the feature selection process without compromising the ability to handle data in an online manner. FairSFS adapts to incoming feature vectors by dynamically adjusting the feature set and discerns the correlations between classification attributes and sensitive attributes from this revised set, thereby forestalling the propagation of sensitive data. Empirical evaluations show that FairSFS not only maintains accuracy that is on par with leading streaming feature selection methods and existing fair feature techniques but also significantly improves fairness metrics.

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