Model Cards for Model Reporting.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., et al. (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT, 220-229.

Type

Conference paper

Intellectual base

Primary research

Year

2019

DOI

10.1145/3287560.3287596

Link

https://doi.org/10.1145/3287560.3287596

Cited from

communication_media_studies, data_science, ethnography_qualitative_methods, statistics_experimental_design

Cited by

12 citations across 12 artifacts.

Each citation links to the sentence it supports in the citing article.

Primes

• Applicability Scope
  • Machine-learning model cards and datasheets declare the input-domain envelope, intended-use class, and known failure modes, flagging out-of-distribution application.

    This sourceProposes model cards that declare a model's intended-use class, input-domain envelope, and known failure modes so consumers can flag out-of-distribution application.

• Boundary Disclosure Card
  • In machine learning, data cards and model cards — explicitly borrowing the nutrition-label idea — travel with datasets and models, carrying composition, intended use, and known limitations.

    This sourceIntroduces the model-card schema carrying intended use, known limitations, and demographic-breakdown evaluation, explicitly motivated by disclosure analogues.

• Memoing
  • Machine learning and data science. Experiment-tracking logs, model cards, ablation rationale, and training-run notes carrying the analyst's in-flight reasoning alongside the trained model.

    This sourceProposes model cards documenting a trained model's intended use, training context, and evaluation rationale alongside the artifact.

• Summary Substance Divergence
  • The pattern recurs in judicial opinions (holding versus reasoning), legislation (bill title versus text), contracts (term sheet versus final agreement), marketing and consumer finance (claims versus fine print), university rankings (the headline number versus the methodology document), polling (topline versus crosstabs), corporate financial communication (earnings-call talking points versus disclosure filings), ESG reporting, AI model cards (intended-use section versus evaluation appendix), and encyclopedia entries (lede versus body).

    This sourceEstablishes the model card's intended-use section versus its benchmarked-evaluation detail — a concrete instance of the summary-versus-substance two-surface structure in AI documentation.

• Transferability Overclaim
  • The prime's interventions apply directly: publish a model card that pre-registers the validated input range, re-evaluate on deployment data before relying on the benchmark (localize before exporting), and install an out-of-distribution monitor as a boundary instrument that fires when inputs leave the validated envelope.

    This sourceProposes model cards documenting the validated operating conditions and intended use of an ML model.

Domain-specific

• Data Card
  • The parallel "Model Card" formulation by Mitchell et al. (2019) extends the idea from datasets to trained models, and Hugging Face has operationalized both as structured metadata fields on its hub, making card-style documentation a routine expectation for shared ML artifacts

    This sourceThe Model Card formulation, proposed by its authors as a complement to Datasheets for Datasets that moves the schema-plus-boundary discipline from datasets to trained models.

Mechanisms

• Distributional-Assumption Card
  • Its strength is that it converts a distribution from a private default into a public, inspectable commitment that survives a hand-off — the same move as a machine-learning model card, which reports a model's intended use

    This sourceProposes model cards that disclose a model's intended uses, data and evaluation context, and limitations so downstream users can judge appropriate use.

• Model Card or Datasheet Linkage
  • Its strength is that it makes an abstraction's warrant inspectable and portable — the reason model cards and datasheets for datasets became standard practice is precisely that models and data outlive and out-travel the knowledge in their authors' heads.

    This sourceThis mechanism is the general form of both.

• Model Card Value Section
  • It extends the model-card tradition of structured reporting from performance into values.

    This sourceIntroduces model cards as structured, transparent documents for reporting trained-model performance.

• Model Limitations Card
  • The card is the artifact that rides along and says, in plain terms, "valid here, uncertain there, do not use beyond this line" — so the output's limits are inseparable from the output.

    This sourceIntroduces model cards as documents that accompany released models and disclose intended and out-of-scope uses, uncertainty, caveats, and recommendations.

• Model or Rule Card
  • Its strength is making a method's assumptions, limits, and ownership legible before anyone relies on it — the practice popularized as model cards for transparent model reporting

    This sourceIntroduces model cards for transparent reporting of assumptions, limits, ownership, and information relevant to method selection.

• Model Specification
  • This is the trap the Model Cards

    This sourceIt is the canonical artifact for stating the scope and assumptions under which a model's outputs are valid, and its whole purpose is to make out-of-distribution misuse visible before it happens.

Verification

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Links previously used in the corpus

Before the registry existed this work was also linked 1 other way.

• https://arxiv.org/abs/1810.03993 ×1

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