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AI Dataset Lifecycle & Takedown Risk Gauge
x402
AI Dataset Lifecycle & Takedown Risk Gauge is a paid API for AI agents from aeml-x402.zeabur.app, paid per call via x402, $0.25/call, status unknown (last checked 2026-09-13).
Returns a multi-leg due-diligence snapshot for a named AI training dataset covering its lifecycle status, legal/ethical takedown pressure, and downstream model lineage exposure, with cross-validated narrative and verifiable citations.
Price / call$0.25
Success—
StatusUnknown
How this service works
[3-LEG] AI dataset takedown chain, one dataset (?entity=laion-5b): leading access pressure from gating and legal notices -> current current availability -> realized lifecycle to gated, withdrawn or deleted (confirmation gate). A sequential chain, not one reading - confidence = how many legs are lit, in order. Sources: host registries and official notices. Descriptive only, never a forecast or advice. No data -> 422 no_charge, not billed.
Output
A structured JSON object with named legs for dataset lifecycle status, legal/ethical takedown pressure signals, and downstream model lineage exposure. Includes a cross-line narrative, provenance metadata with freshness indicators, decision points for rerunning with alternate dataset tags, verifiable Ed25519-signed citations traceable to HuggingFace, Lumen, and Wayback Machine, and honest scope/grade disclosures. Deleted data is confirmed un-reconstructable.
Example use cases
Pre-training compliance check for legal team
“Before we fine-tune on Books3, can you run a due-diligence gauge on the books3 dataset — I need to know if it's been gated or taken down, whether there's active litigation or DMCA pressure, and which existing models would be affected if it disappears?”
Model provenance audit for AI governance
“We're auditing our model's training data lineage — can you check the current status of the MS-Celeb-1M dataset, including its lifecycle stage, any regulatory or ethics actions against it, and what downstream model exposure looks like if it's been deleted?”
Investor DD on AI startup dataset exposure
“I'm doing due diligence on an AI startup that claims to use LAION for training — can you give me the full risk picture on laion-5b, including whether it's been pulled or restricted, any lawsuits or DMCA notices, and what the cascade looks like for models trained on it?”
Technical details
Endpoint
GEThttps://aeml-x402.zeabur.app/gauge/aidataset-regionWhen to prefer
Choose this endpoint when you need a single-call, verifiable, multi-leg due-diligence snapshot on a specific AI training dataset's risk profile — covering lifecycle, legal pressure, and model lineage — rather than manually querying HuggingFace, Lumen, or Wayback Machine separately. Especially useful for AI compliance teams, model builders, and legal counsel who need offline-verifiable citations and provenance traces, not just raw source links.
Side effects
- Unknown or misspelled entity key returns empty or degraded legs
- Dataset with no public takedown record may show sparse legal pressure leg
- Very recently deleted datasets may lag behind Wayback/Lumen indexing
- Provenance freshness may be stale if upstream sources have not updated
- Ed25519 signature verification may fail if key_id or issuer_DID has rotated
- Payment failure (x402) returns 402 before any data is served
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| input | object | |
| output | object |
Output
Schema · Response
{
"type": "json"
}Tags
x402
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