Fairly Accurate: Fairness-aware Multi-group Target Detection in Online Discussion

Abstract

Target-group detection is the task of detecting which group(s) a social media post is ``directed at or about'', with various applications, such as targeted-marketing. In this work, we focus on the fairness implications of target-group detection in the context of toxicity detection, where the perceived harm of a post often depends on which group(s) it targets. Because toxicity is highly contextual, language that appears benign in general may be harmful when targeting specific demographic groups. It is thus important to first detect which group(s) are being {\em targeted} by a post as a precursor to the subsequent task of determining whether the post is toxic given the group(s). Target-group detection is also challenging: a single post may simultaneously target one to many groups, and we must detect groups fairly in order to promote equitable treatment. We show that our proposed approach to {\em fairness-aware multi target-group detection} not only reduces bias across groups, but also achieves competitive predictive performance, outperforming existing fairness-aware baselines. To spur future research on fairness-aware target-group detection and support competitive benchmarking, we also share our code.