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

arXiv: https://arxiv.org/abs/2603.01457 · HTML: https://mlsanity.com/paper/2603.01457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 132 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 30 of 30)

- [Learning the Difference that Makes a Difference with Counterfactually-Augmented Data](https://mlsanity.com/paper/1909.12434/) (2019)
- [Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge](https://mlsanity.com/paper/2410.02736/) (2024)
- [Humans or LLMs as the Judge? A Study on Judgement Biases](https://mlsanity.com/paper/2402.10669/) (2024)
- [Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation](https://mlsanity.com/paper/2204.11788/) (2022)
- [Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support](https://mlsanity.com/paper/2302.12389/) (2023)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025)
- [The Who in XAI: How AI Background Shapes Perceptions of AI Explanations](https://mlsanity.com/paper/2107.13509/) (2021)
- [Believing Anthropomorphism: Examining the Role of Anthropomorphic Cues on Trust in Large Language Models](https://mlsanity.com/paper/2405.06079/) (2024)
- [Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key](https://mlsanity.com/paper/2104.10788/) (2021)
- [Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making](https://mlsanity.com/paper/2209.11812/) (2022)
- [How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study](https://mlsanity.com/paper/2309.10108/) (2023)
- [Human-AI Interactions in Public Sector Decision-Making: "Automation Bias" and "Selective Adherence" to Algorithmic Advice](https://mlsanity.com/paper/2103.02381/) (2021)
- [Toxic Bias: Perspective API Misreads German as More Toxic](https://mlsanity.com/paper/2312.12651/) (2023)
- [Exploring ChatGPT for Toxicity Detection in GitHub](https://mlsanity.com/paper/2312.13105/) (2023)
- [User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions](https://mlsanity.com/paper/2409.00862/) (2024)
- [A Trade-off-centered Framework of Content Moderation](https://mlsanity.com/paper/2206.03450/) (2022)
- [Fair Machine Guidance to Enhance Fair Decision Making in Biased People](https://mlsanity.com/paper/2404.05228/) (2024)
- [DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship](https://mlsanity.com/paper/2411.01844/) (2024)
- [Bending the Automation Bias Curve: A Study of Human and AI-based Decision Making in National Security Contexts](https://mlsanity.com/paper/2306.16507/) (2023)
- [Mediating Community-AI Interaction through Situated Explanation: The Case of AI-Led Moderation](https://mlsanity.com/paper/2008.08202/) (2020)
- [What Lies Beneath? Exploring the Impact of Underlying AI Model Updates in AI-Infused Systems](https://mlsanity.com/paper/2311.10652/) (2023)
- [Debiasing Desire: Addressing Bias & Discrimination on Intimate Platforms](https://mlsanity.com/paper/1809.01563/) (2018)
- [Dimensions of Online Conflict: Towards Modeling Agonism](https://mlsanity.com/paper/2311.03584/) (2023)
- [Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions](https://mlsanity.com/paper/2409.10670/) (2024)
- [Who is Helping Whom? Student Concerns about AI- Teacher Collaboration in Higher Education Classrooms](https://mlsanity.com/paper/2412.14469/) (2024)
- [Towards a Working Definition of Designing Generative User Interfaces](https://mlsanity.com/paper/2505.15049/) (2025)
- [Mitigating Observation Biases in Crowdsourced Label Aggregation](https://mlsanity.com/paper/2302.13100/) (2023)
- [RemiHaven: Integrating "In-Town" and "Out-of-Town" Peers to Provide Personalized Reminiscence Support for Older Drifters](https://mlsanity.com/paper/2503.01358/) (2025)
- ["Harmless to You, Hurtful to Me!": Investigating the Detection of Toxic Languages Grounded in the Perspective of Youth](https://mlsanity.com/paper/2508.02094/) (2025)

## Abstract (arXiv)

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.