Documentation helps buyers discover limits that a demo hides. A good model or dataset card should describe intended use, evaluated conditions and known gaps—but its existence does not certify a system as safe.
Model documentation: what to look for
Ask for model identity and version; training-data broad provenance; supported languages and modalities; known limitations; safety evaluation method; deployment restrictions; and the date measurements were produced. Generic brand-family descriptions are weak substitutes for the precise release used in a production system. If a field is absent, record it as unknown rather than inferring a favorable answer.
Data documentation has a different job
A dataset record should state origin, permission, collection date and methods, labeling practices, retention, exclusions and known coverage gaps. An “open” dataset can still contain privacy or copyright concerns. Document de-identification and data quality assumptions rather than claiming that aggregation eliminates all individual risk.
Use claims that can be checked
A card asserting “supports multilingual use” should identify which languages were tested and for what tasks. A card boasting “safe” should link to a defined safety assessment, benchmark version and failure categories. Vendor-reported tests are useful disclosures but not independent replication. Check whether model aliases and hosted product defaults change after the card was written.
Documentation in an approval workflow
Before procurement, assign a reviewer to unresolved gaps: retention, data residency, licensing, unknown training sources or subgroup reliability. Set a decision deadline and record exceptions with owners. Keep the card attached to a specific deployment configuration and refresh it after major upgrades.
Example: customer FAQ assistant
A service may be strong in English but never have been tested for regional dialects that dominate its user base. The lack of coverage data becomes a planned local evaluation requirement, not a reason to assume equivalent quality.
Good practice is not a warranty
NIST risk guidance encourages documentation and measurement, while public benchmark projects expose methodologies. No standalone card can replace incident monitoring, primary-source verification and human review of consequential decisions.
Sources & Further Reading
- NIST AI Risk Management Framework 1.0 — NIST.
- Holistic Evaluation of Language Models — Stanford CRFM.
- AILuminate benchmarks and methodology — MLCommons.
Guidance varies by jurisdiction and application. This is general education, not legal, medical or other professional advice.