Engineering, production, governance, cost, communication · insight-0282
Ship a model card with intended use and performance by relevant slice
- Evidence type
- practitioner heuristic
- Difficulty
- easy
- Roles
- Data scientist, ML engineer, Applied AI
- Industries
- insurance, healthcare, banking
Summary: situation, action, reason, boundary
Situation
A claims-triage model is handed to an operations team with only an overall accuracy figure.
Action
Publish a model card listing intended uses and out-of-scope uses, training data window, evaluation method, performance by the slices that matter (region, product, customer group), known limitations and contact owner.
Reason
Model cards report performance across relevant conditions and groups and clarify intended use, which discourages use where the model fits poorly.
Boundary
Any model used outside the team that built it.
Symptom or risk
Users apply it to claim types or regions where it was never evaluated, and overall accuracy hides weak slices.
Exceptions
Internal prototypes need a lighter version, but anything with users needs intended use and limits.
Trade-offs
Slice metrics need enough data per slice; report intervals or flag small slices.
Pitfalls
Model cards written once and never updated after retraining.
Lessons and concepts
Claims and their sources
Model cards document benchmarked performance across relevant conditions and groups, intended use contexts and evaluation procedures.