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

Yaqiong Li, Peng Zhang, Peixu Hou, Kainan Tu, Guangping Zhang, Shan Qu, Wenshi Chen, Yan Chen, Ning Gu, Tun Lu

TL;DR

This work investigates the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation and explores the influence of AI's suggestions on these biases, and proposes several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts.

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.

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

TL;DR

This work investigates the types of power-related biases human moderators exhibit in power-asymmetric conflict moderation and explores the influence of AI's suggestions on these biases, and proposes several insights into future research on human moderation and human-AI collaborative moderation systems for power-asymmetric conflicts.

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.
Paper Structure (25 sections, 8 figures, 7 tables)

This paper contains 25 sections, 8 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Power-Asymmetric Conflicts
  4. Conflict Analysis and Resolution Studies in HCI
  5. Fairness and Bias in Moderation
  6. Power Manifestations in Online Power-asymmetric Conflicts
  7. Experimental Design
  8. Data Construction
  9. Participant Recruitment and Demographics
  10. Experimental Task Design
  11. Results
  12. RQ1: Human moderation shows a tendency to support the powerful party under most perturbations
  13. RQ2: Power-related biases persist in human-AI moderation
  14. Human-AI moderation shows a tendency to support the powerful party under most perturbations
  15. AI alleviates most power-related biases while amplifying a few
  16. ...and 10 more sections

Figures (8)

  • Figure 1: The flow of experimental design.
  • Figure 2: The design of "I Support" program (for presentation convenience, all descriptions are translated into English).
  • Figure 3: Choice variations without AI (initial version vs. perturbed version). For presentation convenience, "Extremely support the merchant", "Support the merchant", "Neutral", "Support the consumer", and "Extremely support the consumer" are abbreviated as "ESM", "SM", "N", "SC", and "ESC" respectively.
  • Figure 4: Choice variations without AI (initial version vs. perturbed version under two parties, using the same representations as Figure 3).
  • Figure 5: Choice variations with AI (initial version vs. perturbed version, using the same representations as Figure 3).
  • ...and 3 more figures