Fair Streaming Feature Selection
This addresses fairness issues in real-time data processing for applications like online decision-making, though it is an incremental improvement over existing fair feature techniques.
The paper tackles the problem of bias and discrimination in streaming feature selection by proposing FairSFS, a novel algorithm that dynamically adjusts feature sets to prevent propagation of sensitive attributes, resulting in maintained accuracy and significantly improved fairness metrics compared to existing methods.
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.