Fair Streaming Feature Selection

TL;DR

FairSFS is a new online feature selection algorithm that ensures fairness by reducing bias related to sensitive attributes while maintaining high accuracy in streaming data scenarios.

Contribution

It introduces FairSFS, a novel algorithm that incorporates fairness into streaming feature selection without sacrificing performance.

Findings

FairSFS achieves comparable accuracy to existing methods.

FairSFS significantly improves fairness metrics.

FairSFS effectively discards sensitive attribute correlations.

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.