Definition
A model card is a short, structured document that records the essential facts about a machine-learning model: what it is for, how it performs, where it falls short, and what risks it carries. Introduced by Mitchell and colleagues in 2019, the format sits between a README and a full research paper, giving users enough context to decide whether a model fits their use case.
What a model card contains
A typical card states the model's intended and out-of-scope uses, the data it was trained and evaluated on, performance broken down across relevant groups and conditions, and known limitations, biases, and ethical considerations. Reporting performance across different populations is a deliberate design goal: it surfaces disparities that a single headline accuracy number would hide.
Why it matters
Model cards are now a cornerstone of responsible-AI documentation, expected alongside open-weight releases and referenced in governance frameworks. For someone choosing a model to self-host, the card is the first honest signal of fitness: it should disclose evaluation results, training-data provenance, and the failure modes a deployment must guard against. A missing or vague card is itself a warning.
D-Central treats reading the model card as a basic step in evaluating any AI you intend to run. See also open-weight model and red-teaming (AI).
In Simple Terms
A model card is a short, structured document that records the essential facts about a machine-learning model: what it is for, how it performs, where…