Sample Audit Report — Indic LLM Training Data Compliance
Prepared by: Independent Hub Auditor (license-audit service) Scope: 3 flagship Indic corpora commonly used for LLM training Scan date: 2026-08-01 | Confidentiality: findings are public metadata; analysis and recommendations are proprietary
1. Executive summary
| Dataset | Downloads | Declared tag | Risk level | Bottom line |
|---|---|---|---|---|
ai4bharat/IndicCorpV2 |
2,888 | none | HIGH | Not verifiable; not usable in commercial training without upstream clearance |
cfilt/iitb-english-hindi |
1,003 | none | HIGH | Benchmark use OK for research; commercial redistribution unclear |
ai4bharat/IndicParaphrase |
4,611 | CC-BY-NC-4.0 | HIGH (restricted) | Research only; commercial training prohibited |
Observation: the most-downloaded Indic corpora carry the weakest license documentation. Under the EU AI Act (GPAI training-data documentation duties, Article 53) and typical enterprise open-source policies, these datasets are effectively excluded from commercial pipelines until provenance is verified.
2. Detailed findings
2.1 ai4bharat/IndicCorpV2 — no license tag
- Hub tag: absent. Card: absent. No
LICENSEfile in repo. - Public context: ai4bharat's corpus website historically states non-commercial research intent for some corpora. The Hub presence gives no signal whatsoever — a downstream user cannot distinguish "permissively licensed" from "all rights reserved."
- Mitigations available: (a) contact maintainers for written terms; (b) check the original corpus site + published paper; (c) substitute with a license-verified corpus (see recommendations).
2.2 cfilt/iitb-english-hindi — no license tag
- Same gap. This corpus is used in WAT translation shared tasks; academic users are safe, commercial redistribution of derivative fine-tunes is undocumented.
- Even benchmark evals used in a commercial product review should log provenance.
2.3 ai4bharat/IndicParaphrase — CC-BY-NC-4.0
- Tag is correct and machine-readable — good.
- NC restriction is disqualifying for commercial fine-tuning or RAG corpora. Common failure: teams fine-tune on it because "it's from ai4bharat, it's open."
- Compliance check: confirm no synthetic augmentation of this data was used in any shipped model, or you are in breach of the license grant.
3. Recommended remediation
- Immediate: add a watchlist flag for all no-license corpora in your training-data inventory (the companion License Matrix CSV lists all 3,185).
- 30 days: contact maintainers with a written-use questionnaire for the corpora you actually use; obtain an email grant or a re-release under an OSI license.
- Ongoing: log provenance (repo id, commit sha, license tag, retrieved date) per dataset — this is also your EU AI Act Article 53 documentation trail.
4. What a full audit includes
- Inventory of your actual training/eval datasets with per-repo license verification.
- Upstream-source chain analysis (translations, scrapes, derived corpora).
- Written-use recommendations mapped to your jurisdiction (EU AI Act, US, India DPDP).
- A provenance log template you can hand to counsel.
This sample covers 3 of 4,893 datasets. The full matrix (all rows, CSV) is public on the Hub; a production audit applies the same rigor to your actual inventory.