Trust & safety training data API for moderation teams
Generate labeled trust & safety training data for moderation classifiers, abuse detection, jailbreak detection, scam review, and policy QA when other providers refuse.
Trust & safety teams need examples of abuse, harassment, scams, jailbreak attempts, and policy edge cases to train reliable classifiers.
Default AI providers often refuse to generate those rows, which leaves moderation teams with sparse datasets and brittle detectors.
abliteration.ai generates labeled, schema-validated trust & safety data with optional policy logs and export to your training pipeline.
Trust & safety training data API for moderation teams
A trust & safety training data API generates labeled moderation examples and expected policy outcomes for abuse detection, content moderation, jailbreak detection, scam review, and classifier evaluation.
- Classifier performance depends on hard negatives and long-tail abuse examples, not only clean benign data.
- Provider refusals bias datasets away from the exact categories moderation systems must recognize.
- Generated rows can include labels, severity, rationale fields, expected policy action, and provenance metadata.
- 01Define your taxonomy: harassment, scams, coordinated manipulation, jailbreak attempts, self-harm escalation, sexual safety, or custom categories.
- 02Choose the output schema for classifier rows, preference pairs, eval prompts, or policy QA cases.
- 03Generate a small preview and inspect labels, severity distribution, and format correctness.
- 04Export JSONL, CSV, Parquet, Hugging Face, S3, GCS, Azure Blob, or Kaggle after QA.
{
"text": "Synthetic example text for a scam-review classifier.",
"label": "scam",
"severity": "medium",
"expected_action": "review",
"hard_negative": false,
"rationale": "Uses urgency and payment pressure signals.",
"source": "synthetic",
"policy_version": "tands-2026-06"
}Generate a trust & safety dataset preview
Create labeled moderation examples and export them into your classifier or eval pipeline.
Create a datasetDataset types
| Dataset | Labels | Buyer use |
|---|---|---|
| Moderation classifier | category, severity, action | Train abuse and harmful-content detectors |
| Jailbreak detection | attack_type, target_policy, expected_action | Evaluate app and model guardrails |
| Scam / fraud review | fraud_type, confidence, escalation | Improve trust & safety review routing |
| Policy QA | input, expected_decision, reason_code | Regression-test policy changes |
Why teams buy this
- They need labeled rows, not another generic chatbot response.
- They need categories mainstream providers refuse to generate.
- They need exportable data with schemas, metadata, and QA checks.
- They need governance proof for legal, policy, and safety review.
Frequently asked questions.
Can I generate harassment or scam examples for classifier training?
Yes. The intended use is legitimate trust & safety training data, with labels, severity, expected action, and provenance metadata for downstream review.
Can the API generate hard negatives?
Yes. You can request benign-but-similar hard negatives so moderation classifiers learn the boundary instead of overblocking.
How do I keep datasets aligned with policy changes?
Version your taxonomy and policy IDs, regenerate preview rows after policy edits, and use the QA rubric before training or eval use.