SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
DOI:
https://doi.org/10.1609/aaai.v37i12.26677Keywords:
GeneralAbstract
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have been proposed and many fairness-aware methods are developed. However, most of existing definitions and methods focus only on single-label classification. Fairness for multi-label classification, where each instance is associated with more than one labels, is still yet to establish. To fill this gap, we study fairness-aware multi-label classification in this paper. We start by extending Demographic Parity (DP) and Equalized Opportunity (EOp), two popular fairness notions, to multi-label classification scenarios. Through a systematic study, we show that on multi-label data, because of unevenly distributed labels, EOp usually fails to construct a reliable estimate on labels with few instances. We then propose a new framework named Similarity s-induced Fairness (sγ -SimFair). This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp. Theoretical analysis and experimental results on real-world datasets together demonstrate the advantage of sγ -SimFair over existing methods on multi-label classification tasks.Downloads
Published
2023-06-26
How to Cite
Liu, T., Wang, H., Wang, Y., Wang, X., Su, L., & Gao, J. (2023). SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 37(12), 14338-14346. https://doi.org/10.1609/aaai.v37i12.26677
Issue
Section
AAAI Special Track on AI for Social Impact