Fair Streaming Feature Selection

arXiv:2406.14401v1 Announce Type: new
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, …

abstract algorithms arxiv attributes become biases cs.ai cs.lg current data data streams discrimination fair feature feature selection identification information performance processing real-time streaming time data type