Model Card Skill

A model card is the README for a model: what it does, what it was trained and evaluated on, where it works, and — most importantly — where it doesn't. It turns an opaque artifact into something a reviewer, a downstream team, or a regulator can actually assess. Write it before launch, not after.

Required Inputs

Ask for these only if they aren't already provided:

• Model name & version, owner team, and date.
• What it does — task type (classification, generation, ranking, extraction…) and the decision it informs.
• Intended use & users — the supported use cases, and explicitly the out-of-scope ones.
• Training data — sources, size, time range, and known gaps (link a dataset-datasheet if one exists).
• Evaluation — datasets, metrics, and results, ideally broken down by subgroup/slice.
• Known limitations & risks — failure modes, bias findings, safety concerns.

Output Format

Model Card: [name] v[version]

Owner: [team] · Date: [date] · Status: [in review / production / deprecated]

1. Overview — one paragraph: what the model does, the decision it serves, and who uses it.

2. Intended Use

• In scope: the use cases this model is validated for.
• Out of scope / do not use for: explicit prohibited or unvalidated uses (this section prevents the most harm).
• Users: who is expected to operate or consume it.

3. Training Data — sources, size, time window, labelling method, and known coverage gaps.

4. Evaluation

• Metrics: the primary metric(s) and why they were chosen for this task.
• Overall results: headline numbers vs. a stated baseline.
• Sliced results: a table of the key metric across important subgroups (geography, language, device, demographic where appropriate) — surface where performance drops, don't hide it behind an average.

5. Limitations & Failure Modes — concrete situations where it underperforms or should not be trusted.

6. Ethical Considerations & Bias — fairness findings, sensitive-attribute handling, and mitigations applied.

7. Deployment & Monitoring — serving constraints (latency/cost), the drift/quality signals you'll watch, and the rollback trigger.

Quality Checks

• "Out of scope / do not use for" is filled in with specifics — not left blank
• Evaluation is reported by slice, not just one global average that hides subgroup harm
• Every metric states the baseline it's measured against
• Limitations describe real, concrete failure situations (not "the model may be imperfect")
• A monitoring signal and an explicit rollback trigger are named

Anti-Patterns

• Do not report a single aggregate metric and call evaluation done — averages mask the slices where a model fails worst
• Do not leave "intended use" open-ended — an undefined boundary is an invitation to misuse
• Do not omit known biases because they're uncomfortable — an undocumented risk is a worse liability than a documented one
• Do not present accuracy without the class balance / base rate — 95% accuracy on a 95/5 split is meaningless
• Do not ship without a monitoring plan — a model card without a rollback trigger is a snapshot, not a contract

Based On

Model Cards for Model Reporting (Mitchell et al., 2019) and the model-documentation practice used in responsible-AI reviews.