Fairness-Aware Multi-Group Target Detection in Online Discussion

TL;DR

This paper introduces a fairness-aware multi-group target detection method for toxicity detection in online discussions, improving bias mitigation and predictive accuracy.

Contribution

It presents a novel approach that reduces bias across groups while maintaining strong predictive performance, surpassing existing fairness-aware baselines.

Findings

Reduces bias across demographic groups in toxicity detection

Achieves higher predictive accuracy than existing fairness-aware methods

Shares code publicly to promote reproducibility

Abstract

Target-group detection is the task of detecting which group(s) a piece of content is ``directed at or about''. Applications include targeted marketing, content recommendation, and group-specific content assessment. Key challenges include: 1) that a single post may target multiple groups; and 2) ensuring consistent detection accuracy across groups for fairness. In this work, we investigate fairness implications of target-group detection in the context of toxicity detection, where the perceived harm of a social media post often depends on which group(s) it targets. Because toxicity is highly contextual, language that appears benign in general can be harmful when targeting specific demographic groups. We show our {\em fairness-aware multi-group target detection} approach both reduces bias across groups and shows strong predictive performance, surpassing existing fairness-aware baselines. To enable reproducibility and spur future work, we share our code online.