Why it matters

Model cards are becoming compliance requirements (EU AI Act). Understanding them prepares for regulatory environment and enables good citizen ML.

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The architecture

Structure: intended use, out-of-scope use, training data, evaluation, biases, environmental impact, license.

Metadata YAML: front matter for machine-readable info (license, language, task).

Model card sectionsIntended usepositive examplesLimitationsknown failure modesLicense + attributionlegalWell-written cards prevent misuse; regulators increasingly require them
Model card structure.
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How it works end to end

Evaluation: report metrics on standard benchmarks + your task. Include comparison baselines.

Bias/fairness: which demographics tested; known biases.

Environmental: compute for training (kWh, CO2 emissions). Green ML.

License: explicit terms. Commercial vs research vs Apache 2.