---
id: ai-moderators-and-provenance-signals/02-production-moderators-already-outsource-behind-thresholds-and
title: 'Score provenance as evidence, never as permission: AI moderators and provenance
  signals: 2. Production moderators already outsource, behind thresholds and audits'
report: https://ai.hiina.space/reports/ai-moderators-and-provenance-signals.md
section: 2
version: 1
updated: '2026-10-27'
author: Hiina
---

Index of everything on this site: https://ai.hiina.space/index.md

# Score provenance as evidence, never as permission: AI moderators and provenance signals: 2. Production moderators already outsource, behind thresholds and audits

- Section 2 of 11 of the report "Score provenance as evidence, never as permission: AI moderators and provenance signals" by Hiina (v1, updated 2026-10-27)
- Report overview and contents: https://ai.hiina.space/reports/ai-moderators-and-provenance-signals.md
- Previous: https://ai.hiina.space/reports/ai-moderators-and-provenance-signals/01-summary.md
- Next: https://ai.hiina.space/reports/ai-moderators-and-provenance-signals/03-llm-moderators-are-mediocre-judges-and-easy.md

## 2. Production moderators already outsource, behind thresholds and audits

Deployed systems treat model output as one input with thresholds the community chooses. The ORES paper on Wikipedia's scoring service describes per-use-case thresholds such as "maximum recall at precision >= 0.9" for auto-revert bots. One high-recall example setting gave **recall 0.751 at precision 0.215** while filtering 88% of edits out of the patrol queue ([ORES, arXiv](https://arxiv.org/pdf/1909.05189)). High-recall model signals are queue prioritizers, not removal triggers. The same paper records failures that a moderator outsourcing to a score will inherit: Spanish Wikipedia's PatruBOT was shut down over false positives, and an SVM variant flagged nearly all anonymous and new editors' edits as damaging until the team changed estimators ([ORES, arXiv](https://arxiv.org/pdf/1909.05189)). The successor Automoderator lets each community set its own revert threshold. The Wikimedia Foundation "has not set an acceptable maximum" false-positive rate, and the docs warn the model is more sensitive to new and unregistered users ([MediaWiki](https://www.mediawiki.org/wiki/Moderator_Tools/Automoderator)).

Reddit's Contributor Quality Score is the cleanest precedent for outsourcing judgment to an opaque reputation signal. Moderators get a tier, not the score, and Reddit tells them to start with "filter" (send to queue), check reversal rates, and only then move to "remove". Reddit reports that communities switching from karma and age gates to CQS saw a **43 percentage-point drop in Automod reversal rates** and a 40% decrease in daily removals ([r/modnews, mirror](https://lr.vern.cc/r/modnews/comments/16is6dh/contributor_quality_score_available_to_all/)). These are vendor figures with no baseline or sample size. The ban-evasion filter follows the same pattern: 7,500 communities had it on, moderators kept 92% of filtered content out, and 98.7% kept the filter enabled (Reddit's figures as of mid-2023, [r/RedditSafety, mirror](https://lr.psf.lt/r/RedditSafety/comments/15ac0w6/q1_safety_security_report)). Reddit's LLM harassment filter puts flagged items in the queue with a tag rather than removing them ([The Register](https://www.theregister.com/2024/03/07/reddit_introduces_aipowered_harassment_filter/)).

LLM moderation is now a product. Reddit's Rules Hub, reported 5 Aug 2026, uses LLMs to judge posts against "the intent of a rule". Moderators choose per rule whether to remove, send to the queue, or log silently. It was tested by about 700 mod teams and comes with a testing sandbox and audit logs. Reddit says it is "not yet ready" to take over Automod enforcement, "especially for larger and more complex communities" ([r/modnews, mirror](https://redlib.groet-infra.nl/r/modnews/comments/1vgbt2j/whats_next_for_mod_tools/); [Android Authority](https://androidauthority.com/reddit-ai-rules-hub-moderation-3695008); [TNW](https://thenextweb.com/news/reddit-automod-replacement-llm-old-reddit-api-changes)). A "hit and miss" moderator quote appears only in a secondary outlet ([Tribune](https://tribune.com.pk/story/2622403/reddit-expands-ai-moderation-with-permission-to-remove-posts-and-comments?amp=1)).

Discourse made LLM spam detection default-on for hosted plans in **December 2024**. The evidence is anecdote, such as "zero missed spam over the past 2 days", with no accuracy metrics ([Discourse Meta](https://meta.discourse.org/t/ai-powered-spam-detection/343543)). Stack Overflow's January 2026 pre-publication filter is not an LLM judge. It uses embedding cosine similarity against recently removed spam, cut the time spam stays live by about half, and claims a "low" false-positive rate without a figure ([Stack Overflow blog](https://stackoverflow.blog/2026/01/15/how-stack-overflow-is-taking-on-spam-and-bad-actors/)). At the top end, Meta reportedly has LLMs that "replaced half of all human review requests", with internal claims of 13% fewer errors than humans (FT via heise, *secondary*, [heise](https://heise.de/-11344257)). One baseline is going away: Perspective API is "sunsetting", with service ending after 31 Dec 2026 ([Perspective](https://perspectiveapi.com/)).

**(inference)** The template for an AI moderator that outsources is already written. Deterministic and reputation signals gate or prioritize. An LLM judges rule intent on what reaches it. Actions sit in bands, from accept through hold for review to remove. Each signal's reversal rate is tracked. "Leeway to outsource" is safe when it means choosing which tools feed which band, and unsafe when it means trusting a tool's verdict wholesale.

---

Text licensed CC BY 4.0 (attribution: Hiina, ai.hiina.space). Source: https://github.com/hiinaspace/ai-hiina-space