Model Cards and Model Documentation

Overview

A model card is a structured description of a trained model: what it is for, what data it saw, how it was measured, and where it should not be used.

If every field is superlative, it is not documentation. It is a brochure.

Definition

Useful fields include intended use, training data sources at an auditable level, metrics on stated slices, and out-of-scope uses.

Versioning belongs on the card: which checkpoint, which code, which data snapshot.

This wiki page describes the genre. It is not a legal template for every jurisdiction.

Why the distinction matters

Without a card, a model is a file with rumours attached. With a dishonest card, it is a file with rumours on letterhead.

Downstream users need out-of-scope lines more than they need adjectives.

Core pieces

• Intended use.
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• Data notes.
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• Metrics and slices.
• Limitations and out-of-scope uses.
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• Version and contact for the artifact.

If a tutorial skips these pieces and jumps to a demo, you are watching a product, not reading a definition.

Worked intuition

A speech model trained on read news is not “for all voices.” A card that says so prevents a sloppy demo in a clinic hallway.

That is the job: prevent sloppy reuse, not win a design award.

Common confusions

• Empty “N/A” on data.
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• Metrics without a split.
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• Copy-pasting another model’s card.
• Hiding paid evaluation setups.
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Limits

A card cannot make an unfit model fit. It can make the unfitness visible.

Some data cannot be described in detail; then say what you cannot say, rather than inventing a corpus fairy tale.

Practical checks

1. Fill intended use and out-of-scope first.
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2. Attach the checkpoint hash.
3. Report at least one slice that is likely to be worse.
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4. Date the card.
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What a careful page refuses

It refuses fake precision, fake timelines, and vendor adjectives that are not part of the definition.

Some data cannot be described in detail; then say what you cannot say, rather than inventing a corpus fairy tale.

Related pages

See also: evaluation metrics, limitations of current AI, training and inference.

Glossary

• Intended use: the job you are willing to stand behind.
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• Slice: a subset on which you report a metric.
• Artifact: the files that actually run.
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How to use this wiki page

Read the definition, then the confusions, then the checks. The FAQ is last on purpose: it should not replace the definition.

If you cite this page, cite the limitation that matches your use, not only the first sentence.

FAQ

Is a README a model card?

If it has these fields, it may function as one. The name is less important than the content.

Must it be public?

Public is better for reuse. Internal cards still help teams.

Does a card replace evaluation?

No. It reports evaluation.

Why this page exists in the collection

Model Cards and Model Documentation sits in a Article / Wiki slot with category Computer science / Artificial intelligence. That pairing is not decoration: readers should be able to tell a research note from a listing, and a home page from a wiki overview, before they quote a sentence out of context.

The one-line job of the page is this: Wiki page on documenting models: intended use, data, metrics, and out-of-scope uses.

If you only remember one constraint, remember the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

The page is written for computer science readers who will either teach from it, cite it, or use it as a map. It is not written as a press release and it does not invent measurements that were not collected.

Scope and non-scope, stated slowly

In scope: the practice and documents around Computer science, Artificial intelligence, model cards, documentation. Out of scope: ranking offices, promising outcomes, or turning a classroom into a market.

A useful test is whether a sentence still holds if you remove adjectives. “Intended use.” is the kind of object this page is willing to talk about because it can be pointed at.

Another object on the table is “Data notes.”. If your question is actually about something else—private casework, live filings, clinical advice, or product pricing—stop and go to a qualified channel.

Non-scope also includes gossip about named minors, unnamed “secret” datasets, and any request to hide a limitation because it makes the story less tidy.

Walking through the checklist in full sentences

Item 1. Intended use. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 2. Data notes. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 3. Metrics and slices. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 4. Limitations and out-of-scope uses. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 5. Version and contact for the artifact. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 6. Empty “N/A” on data. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 7. Metrics without a split. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 8. Copy-pasting another model’s card. Treat this as something you could put on a table in a meeting about Model Cards and Model Documentation. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

A longer narrative of the problem

People usually meet Model Cards and Model Documentation as a short slogan. The slogan travels faster than the log. Then a team is surprised when a term ends and the only remaining trace is a folder of unused files.

The longer story is operational. Someone has to name the text, the hour, the owner, and the thing students or readers will produce. Without that, Computer science, Artificial intelligence, model cards, documentation becomes wallpaper.

Consider a week in which Intended use. is supposed to happen, but Data notes. is competing for the same hour. The honest publication names the collision instead of adding a new poster.

Consider also the quiet failure: the work is done, but nobody can find it next month because the filename is “final-final-v3”. Documentation is part of the method, not an afterthought for Model Cards and Model Documentation.

None of this requires a new brand of software. It requires a calendar, a named artifact, and a sentence about what will not be claimed. That is the tone of this page.

Worked scenario A: a careful trial

A small team decides to trial one idea from Model Cards and Model Documentation for four weeks, not a year. They write the question in one sentence copied from the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

Week 1 is setup: they identify the artifact that will count as “done.” It should be as concrete as Intended use.. They also write the exclusion: they will not claim effects they did not measure.

Week 2 is the first real run. They expect friction around Data notes.. They log what was skipped and why, in language a substitute colleague could understand.

Week 3 is a repair week. They drop one extra ambition so Metrics and slices. can actually finish. Repair is not failure; it is the method.

Week 4 is a write-up of two pages: what happened, what they will keep, what they will not repeat. They cite this page as a map, not as proof.

Worked scenario B: the over-scoped version that fails

A different team announces Model Cards and Model Documentation as a whole-institution priority in the same week they have reports, a public event, and a system migration. Nothing is named as the single artifact.

They create a dashboard. The dashboard cannot answer whether Intended use. occurred. It can only show that a file was uploaded.

By week six the original lead—Page type: Article / Wiki · Category: Computer science / Artificial intelligence—is no longer mentioned in meetings. People mention “the initiative.” Initiatives do not leave notebooks.

The recovery is embarrassing and simple: shrink back to one unit, one owner, one collected task, and the limits already written on this page.

A twelve-week implementation sketch

1. Week 1: Name the question Model Cards and Model Documentation is actually asking.
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2. Week 2: Inventory current documents related to Computer science, Artificial intelligence, model cards, documentation.
3. Week 3: Pick one artifact as concrete as: Intended use..
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4. Week 4: Write the non-claims in language copied from this page’s limits.
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5. Week 5: Run a tiny version that still includes Data notes..
6. Week 6: Log skips; do not hide them in a highlight reel.
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7. Week 7: Repair the calendar so Metrics and slices. can finish.
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8. Week 8: Share a two-page note with a colleague who was not in the room.
9. Week 9: Decide whether to stop, continue, or redesign.
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10. Week 10: If continuing, freeze the definition of “done” for the next month.
11. Week 11: Check that citations still point at dated sources, not at rumours.
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12. Week 12: Retire leftover files that contradict the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence
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This calendar is a sketch for Model Cards and Model Documentation, not a contract. If a public deadline in computer science collides with a week, move the week—do not pretend both happened.

If you skip logging, you are back to slogans. The sketch exists to make skipping visible.

Documentation pack

• A one-sentence question taken from Model Cards and Model Documentation.
• The dated lead as published: Page type: Article / Wiki · Category: Computer science / Artificial intelligence
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• A list of in-scope objects, starting with Intended use..
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• A list of out-of-scope requests (advice, rankings, invented rates).
• Names of owners for Data notes. and a substitute if they are away.
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• A filename convention that includes a date.
• A citation line that includes limits.
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• Links to sibling pages in Computer science.
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• A retirement note for superseded files.
• A short glossary so newcomers do not invent synonyms.
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If the pack cannot fit in a folder a new colleague can open in five minutes, it is too baroque for Model Cards and Model Documentation.

Pretty templates are optional. Dates and owners are not.

Error catalog

• Asking the page to do casework, medical advice, or live filings.
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• Mixing page type Article / Wiki with a different genre in the same citation.
• Inventing a percentage because a meeting wanted a percentage.
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• Hiding the collision between Data notes. and a hard calendar event.
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• Treating Intended use. as optional theatre while keeping the slogan.
• Letting an undated PDF outrank the dated page.
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• Quoting Model Cards and Model Documentation as if it measured an outcome it explicitly refused to measure.
• Publishing identifiable information that the method said to remove.
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• Citing an unofficial look-alike domain as the primary source.
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• Scaling across all of computer science before a four-week trial exists.

Each error is recoverable if you name it early. It is expensive if it becomes the public story of the work.

The cheapest prevention for Model Cards and Model Documentation is to reread the non-claims before you present.

Glossary for this page

• Model Cards and Model Documentation — the document you are reading, with page type Article / Wiki and category Computer science / Artificial intelligence.
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• Artifact — a thing you could hold up, such as: Intended use.
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• Lead — the opening claim: Page type: Article / Wiki · Category: Computer science / Artificial intelligence
• Limit — a sentence that forbids a nicer claim than the method can carry.
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• Computer science — the home section of this page, not a licence to speak for every office in the world.
• Date — the difference between a publication and a rumour.
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• Owner — the person who can change Data notes. without a mystery committee.
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• Sibling page — another title in the same section, listed below when available.

Reader checklist before you cite or adopt

1. Can you state the job of Model Cards and Model Documentation without adjectives?
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2. Can you point at Intended use. in a real folder or classroom?
3. Is every number (if any) sourced, or did you add none because none were collected?
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4. Does the citation include the limit that belongs with Computer science, Artificial intelligence, model cards, documentation?
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5. Would a substitute colleague know what “done” looks like next week?
6. Have you avoided promising a ranking, a cure, or a guaranteed placement?
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7. Is the page type still honestly Article / Wiki?
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8. Is the category still honestly Computer science / Artificial intelligence?

If you fail two checks, do not cite yet. Fix the file or shrink the claim.

This checklist is part of Model Cards and Model Documentation, not a generic poster.

What “good enough” looks like without fake scores

Good enough for Model Cards and Model Documentation is a dated artifact, a named owner, and a next step that survived contact with a calendar.

It is not a launch photograph. It is not a dashboard that cannot answer whether Intended use. happened.

It is certainly not a claim that Computer science, Artificial intelligence, model cards, documentation has been “solved.” Solved is a word this collection tries not to use.

If you need a number, collect one that matches the question, then publish the instrument. Until then, write in sentences.

Teaching notes

If you teach Model Cards and Model Documentation, give students a primary object first: a form, a lab page, a syllabus line, a model card, a gazette. Then give them this page as a map of how to talk about that object.

A good thirty-minute seminar: (1) read the lead, (2) mark the non-claims, (3) try to apply Intended use. to a public document you did not write.

Do not ask students to harvest private data. Do not ask them to impersonate an office. Do not ask them to produce a rate you would not defend.

Assessment can be a two-page memo that cites this page and one official source, with the date of capture written on the first line. That is enough to see whether computer science literacy is happening.

For information officers and editors

If you maintain public pages in computer science, steal the habits, not the adjectives: date, owner, next step, non-claim.

Model Cards and Model Documentation will age. Put a review month on it. If you cannot review it, do not let it remain the featured link.

When legal, medical, or emergency readers arrive, your first job is to send them to a qualified channel. Education pages that pretend to be those channels cause harm.

When you quote Model Cards and Model Documentation in a newsletter, quote a limit next to the attractive sentence. Attractive sentences travel; limits do not, unless you chain them.

Notes on wiki genre

A wiki overview defines, distinguishes, and lists failure modes. It does not sell a library or a timeline to imaginary general intelligence.

Model Cards and Model Documentation should be cited for the distinction it draws, not as proof that a product works.

If a tutorial skips evaluation and jumps to a demo, it is not this page.

Update the glossary if a word starts meaning three things in your course. Do not pretend the field is settled.

Related pages in this collection

• Rule-Based Systems and Statistical AI — Wiki article contrasting rule-based systems with statistical (learned) methods in AI history and practice.
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• Supervised Learning — Wiki-style overview of supervised learning: labeled examples, a model, and a test the labels did not train on.
• Training and Inference — Wiki article distinguishing training (fitting weights) from inference (using a fixed model).
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• Limitations of Current AI Systems — Wiki-style catalog of limitations: distribution shift, hallucinations in generation, and cost of evaluation.
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• Overfitting and Regularization — Wiki article on overfitting: fitting the training sample too closely, and regularization as a family of restraints.

These titles share the Computer science section with Model Cards and Model Documentation. They are not duplicates. Read the page type before you mix citations.

If a sibling contradicts this page, prefer the dated limits on each page rather than blending them into a mash-up claim.

Plain-language recap

Model Cards and Model Documentation is a Article / Wiki page in Computer science / Artificial intelligence. Its job is: Wiki page on documenting models: intended use, data, metrics, and out-of-scope uses.

Do the concrete thing (Intended use.). Write down what you will not claim. Date the file. Name an owner for Data notes..

Do not invent rates. Do not use this page as a clinic, a court, or a marketplace. Do not strip the limits off the attractive sentences.

If you do only that, the collection has done enough work for one reading.

Versioning and review

When you locally adapt Model Cards and Model Documentation, keep a version line: date, editor, what changed, what did not.

A change to the lead is a new document. A change to an example can be a minor note.

Review at least when the surrounding computer science calendar jumps (new term, new statute text, new dataset version).

If nobody is named to review it, the page is already on its way to becoming folklore.