# Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation

arXiv: https://arxiv.org/abs/2410.23448 · HTML: https://mlsanity.com/paper/2410.23448/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Toward a Perspectivist Turn in Ground Truthing for Predictive Computing](https://mlsanity.com/paper/2109.04270/) (2021)
- [Jury Learning: Integrating Dissenting Voices into Machine Learning Models](https://mlsanity.com/paper/2202.02950/) (2022)
- [When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks](https://mlsanity.com/paper/2305.06626/) (2023)
- [Direct Uncertainty Prediction for Medical Second Opinions](https://mlsanity.com/paper/1807.01771/) (2018)
- [The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels](https://mlsanity.com/paper/2405.05860/) (2024)
- [Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor](https://mlsanity.com/paper/2305.10374/) (2023)
- [Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia](https://mlsanity.com/paper/2402.14147/) (2024)
- [AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection](https://mlsanity.com/paper/2212.10405/) (2022)
- [Automated Content Moderation Increases Adherence to Community Guidelines](https://mlsanity.com/paper/2210.10454/) (2022)
- [Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement](https://mlsanity.com/paper/2305.01615/) (2023)
- [Corpus Considerations for Annotator Modeling and Scaling](https://mlsanity.com/paper/2404.02340/) (2024)
- [Principled Evaluation with Human Labels: One Rater at a Time and Rater Equivalence](https://mlsanity.com/paper/2106.01254/) (2021)
- [Measuring the Monetary Value of Online Volunteer Work](https://mlsanity.com/paper/2205.14528/) (2022)
- [One Style Does Not Regulate All: Moderation Practices in Public and Private WhatsApp Groups](https://mlsanity.com/paper/2401.08091/) (2024)

## Built on by (most cited first, 7 of 7)

- [MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance](https://mlsanity.com/paper/2505.14483/) (2025)
- [The Gray Area: Characterizing Moderator Disagreement on Reddit](https://mlsanity.com/paper/2601.01620/) (2026)
- [From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communities](https://mlsanity.com/paper/2604.03920/) (2026)
- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions](https://mlsanity.com/paper/2607.28643/) (2026)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Asking For It: Question-Answering for Predicting Rule Infractions in Online Content Moderation](https://mlsanity.com/paper/2510.06350/) (2025)

## Abstract (arXiv)

Research into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it.