Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts

TL;DR

This study investigates how human moderators exhibit biases in online conflicts where power asymmetry exists, and examines how AI suggestions influence these biases, revealing that AI can both reduce and amplify certain moderation biases.

Contribution

The paper explores moderation biases in power-asymmetric conflicts and evaluates the impact of AI assistance, providing new insights into human-AI collaborative moderation systems.

Findings

Biases favoring the powerful party are common in moderation.

AI assistance reduces most human biases.

AI can also amplify some biases.

Abstract

Online power-asymmetric conflicts are prevalent, and most platforms rely on human moderators to conduct moderation currently. Previous studies have been continuously focusing on investigating human moderation biases in different scenarios, while moderation biases under power-asymmetric conflicts remain unexplored. Therefore, we aim to investigate the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation (RQ1) and further explore the influence of AI's suggestions on these biases (RQ2). For this goal, we conducted a mixed design experiment with 50 participants by leveraging the real conflicts between consumers and merchants as a scenario. Results suggest several biases towards supporting the powerful party within these two moderation modes. AI assistance alleviates most biases of human moderation, but also amplifies a few. Based on these results, we propose several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts.