Skip to main content
v2026.11,772 entries · CC-BY 4.0
Dictionary termTrack CStablev2026.2

Algorithm card

A documentation artefact describing the algorithmic method or family (e.g., a particular gradient-boosting estimator, a clustering algorithm) independent of any particular trained instance, including inductive biases, assumptions, complexity, and intended use cases.

ByCASRAI Editorial Board
· Last updated 22 Aug 2026
Share this

Ask CASRAI · included with Regulatory Radar

Ask about Algorithm card

Ask CASRAI answers research-administration questions and cites the passages behind every claim — and says so when the corpus does not cover something, instead of guessing. It comes with a Regulatory Radar subscription at $29 a month, alongside the daily digest of regulatory changes and the dashboard of what changed.

150 questions a day, on this site, over the API, or inside your own tools through the CASRAI MCP server.

Everything CASRAI publishes — this page, the dictionary, the guides and the news — stays free to read, with no account and no card.

Examples

Worked examples

  • Is an instance

    An algorithm card for XGBoost describing its inductive biases, suitability for tabular data, and known limitations on high-dimensional sparse data.

  • Is an instance

    An algorithm card for k-means describing the convexity assumption.

Counter-examples

Looks similar, but isn't

  • Not an instance

    A model card for a specific trained XGBoost classifier.

  • Not an instance

    A textbook chapter on the algorithm.

Editorial commentary

An algorithm card documents an algorithmic method or method family — a particular gradient-boosting estimator, a clustering algorithm, a specific attention mechanism — independent of any single trained instance of it. It covers inductive biases, underlying assumptions, computational complexity, known failure modes, and the classes of problem the method is and is not suited to.

How this differs from a model card

The distinction is instance versus class. A model card describes one specific trained model — a particular set of learned weights, trained on particular data, with measured performance on particular benchmarks. An algorithm card describes the method before any training happens at all: it would be the same document whether the method was ultimately trained on one dataset or a thousand. A research team choosing between two candidate methods for a task reads algorithm cards; a team evaluating whether a specific already-trained model is fit for their use reads model cards.

Relationship to use cards and system cards

A use card documents a specific deployment of a specific trained model; a system card documents the full system a deployment sits inside. Algorithm cards sit at the opposite end of that spectrum — the most abstract, least deployment-specific layer of the documentation family.

Why it matters

Algorithm cards are most useful during method selection, well before a specific model exists to document: a methods section citing “we selected algorithm X because of property Y, per its algorithm card” is a stronger, more falsifiable claim than an undocumented choice.

References

Also known as

method card

Machine-readable encodings

Use in your systems

JATS XML <role> element
xml
<role vocab="credit"
      vocab-identifier="https://casrai.org/dictionary/"
      vocab-term="Algorithm card"
      vocab-term-identifier="https://casrai.org/dictionary/term/algorithm-card" />
Schema.org DefinedTerm (JSON-LD)
json
{
  "@context": "https://schema.org",
  "@type": "DefinedTerm",
  "@id": "https://casrai.org/dictionary/term/algorithm-card",
  "name": "Algorithm card",
  "identifier": "https://casrai.org/dictionary/term/algorithm-card",
  "description": "A documentation artefact describing the algorithmic method or family (e.g., a particular gradient-boosting estimator, a clustering algorithm) independent of any particular trained instance, including inductive biases, assumptions, complexity, and intended use cases.",
  "inDefinedTermSet": "https://casrai.org/dictionary/domain/ai-ml-research-outputs#set",
  "url": "https://casrai.org/dictionary/term/algorithm-card",
  "sameAs": [
    "method card"
  ],
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "publisher": {
    "@id": "https://casrai.org/#organization"
  },
  "author": {
    "@id": "https://casrai.org/#editorial-team"
  },
  "datePublished": "2026-05-21T02:22:50",
  "dateModified": "2026-08-22T15:43:55",
  "inLanguage": "en-GB",
  "isAccessibleForFree": true
}

Referenced across the research world

University of Cambridge logoColumbia University logoCrossref logoUniversity of Edinburgh logoHarvard University logoUniversity of Oxford logoPrinceton University logoStanford School of Medicine logoUniversity College London logoORCID logoUniversity of Cambridge logoColumbia University logoCrossref logoUniversity of Edinburgh logoHarvard University logoUniversity of Oxford logoPrinceton University logoStanford School of Medicine logoUniversity College London logoORCID logo
  • University of Cambridge logo
  • Columbia University logo
  • Crossref logo
  • University of Edinburgh logo
  • Harvard University logo
  • University of Oxford logo
  • Princeton University logo
  • Stanford School of Medicine logo
  • University College London logo
  • ORCID logo

View CASRAI adoption →

Regulatory Radar

Stop finding out after the fact

$29/month, cancel anytime. Daily digest updates from our analysis, a dashboard holding the same items, and a cited assistant for everything they raise.

  • Federal Register, Federal Register+, Grants.gov, Regulations.gov, NSF News, UKRI, plus CASRAI’s own published content.
  • 72,264 indexed passages, and every answer cites the ones it drew on.