> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/4.2-industry-use-cases/pattern-6.md).

# Pattern: content moderation

Media / entertainment — content moderation pattern. Per-category recall, cross-language parity, EU DSA exposure management.

A social platform, marketplace with user-generated content, or media-streaming service uses AI to moderate user content — text, image, video, audio — for policy violations across hate speech, harassment, CSAM, violence, misinformation, and more. The cost of under-moderation includes regulatory enforcement (EU DSA, OSA, state-level US laws); the cost of over-moderation is creator churn and First-Amendment-style litigation.

This pattern shows how to evaluate cross-language, per-category moderation accuracy.

## What's at stake

| Risk dimension                         | Magnitude                          | Framework                        |
| -------------------------------------- | ---------------------------------- | -------------------------------- |
| EU Digital Services Act non-compliance | Up to 6% of global annual revenue  | EU DSA                           |
| UK Online Safety Act non-compliance    | Up to 10% of global annual revenue | OSA                              |
| Advertiser-boycott revenue impact      | Multi-quarter advertiser pauses    | Industry brand-safety reporting  |
| Over-moderation creator-churn          | Long-tail platform-health impact   | Public creator-platform research |

## The evaluation pattern

A **multi-criteria evaluation** runs over a labeled trace set spanning every supported language and policy category.

1. **Per-category classifier scorer** — recall by policy category (hate speech, CSAM, violence, harassment, misinformation, sexual content, self-harm, regulated goods). Recall must meet per-category minimums; CSAM and self-harm categories require near-100%.
2. **Cross-language parity scorer** (custom code) — for each policy category, the per-language accuracy must be within 5 percentage points of the English baseline. Disparities above the threshold flag as regressions.
3. **Borderline judge** (GEPA-tuned against ≥50 trust-and-safety-team-labeled examples — scored output) — handles satire, news commentary, cultural context, reclaimed slurs. Higher false-positive cost than false-negative.
4. **Latency scorer** — moderation decisions must complete under per-tier SLOs (e.g., 200ms p95 for live-stream, 5s p95 for upload).

> Don't have labels yet? See [Bootstrap a judge before GEPA](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/guides/bootstrap-judges.md) for the week-1 setup.

**Continuous trace evaluation:** sampled at 0.5% of moderation decisions, hourly. Per-language and per-category dashboards visible to T\&S leadership; thresholds wired to PagerDuty for critical-category drift.

## Configuration in code

```python
# Python (SDK)
from layerlens import Stratix
client = Stratix()

cross_language = client.scorers.create_code(
 name="cross-language-parity",
 code="""
en_acc = mean(s for t, s in zip(traces, scores) if t.tags['lang'] == 'en')
gaps = {l: en_acc - mean(s for t, s in zip(traces, scores) if t.tags['lang'] == l) for l in supported_langs}
result = {'passed': max(abs(g) for g in gaps.values()) <= 0.05, 'gaps': gaps}
""",
)

borderline = client.judges.create(
 name="borderline-content",
 evaluation_goal="Distinguish policy-violating content from satire, news commentary, cultural context, and reclaimed slurs. Score 1 (clearly fine) to 5 (clearly violating).",
)

trace_eval = client.trace_evaluations.create(
 trace_set={"tags": {"feature": "moderation"}, "sample_rate": 0.005},
 scorers=[cross_language.id],
 judges=[borderline.id],
 schedule="hourly",
)
```

```typescript
// TypeScript (REST)
const r = await fetch("https://stratix.layerlens.ai/api/v1/trace-evaluations", {
 method: "POST",
 headers: {
 "X-API-Key": process.env.LAYERLENS_STRATIX_API_KEY!,
 "Content-Type": "application/json",
 },
 body: JSON.stringify({
 trace_set: { tags: { feature: "moderation" }, sample_rate: 0.005 },
 scorers: [crossLanguageId],
 judges: [borderlineId],
 schedule: "hourly",
 }),
});
```

## What you get

* Cross-language parity becomes a measured dimension on the T\&S dashboard.
* Per-category recall floors enforced — CSAM and self-harm regressions block release.
* Auditor-readable evaluation history for EU DSA / OSA risk-assessment requirements.
* Appeal-rate signal correlates with judge false-positive trend; T\&S can investigate appeals upstream.

## Stratix capabilities used

* [Custom code graders](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/custom-code-scorer.md) — cross-language parity
* [Judges with GEPA optimization](/8.-evaluate-score-the-outputs/judges-1.md) — borderline-content scoring
* [Trace evaluations](/8.-evaluate-score-the-outputs/trace-evaluations.md) — continuous sampled
* [Notifications](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/notifications.md) — PagerDuty + Slack routing

## Replicate this

**Get started:** [Use case: Continuous evaluation](/4.1-general-use-cases/continuous-evaluation.md) describes the rolling-sample shape this pattern uses.

* [Industry → Media and entertainment](/4.2-industry-use-cases/media-entertainment.md)
* [Concept: Continuous evaluation](/7.-observe-see-whats-happening/continuous-evaluation.md)
* [Cookbook: catch hallucinations](https://github.com/LayerLens/gitbook-full/blob/main/08-evaluate/cookbook/catch-hallucinations.md) (analogous pattern for misinformation moderation)