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 ($s_\gamma$-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 over existing methods $s_\gamma$-SimFair on multi-label
classification tasks.

本研究探索了在多标签分类任务中公平意识的构建，并提出了一种新的框架，名为相似性诱导公平。该框架利用具有相似标签的数据来实现更好的稳定性，在多标签数据上表现出比现有方法更好的性能。