5.1 — Model Cards
Model Cards (Mitchell et al., 2019) are standardized documentation for trained ML models. They provide essential transparency about a model's capabilities, limitations, and appropriate use contexts.
Developer, version, model type, architecture, license, contact information.
Primary use cases, downstream applications, and explicitly out-of-scope uses.
Relevant demographic, environmental, and instrumentation factors that affect performance.
Performance measures selected, thresholds for acceptable performance, variation across factors.
Datasets used for evaluation, preprocessing steps, motivation for selection.
Overview of training data (no need to expose proprietary details), size, characteristics.
Sensitive use cases, known limitations, potential for misuse, societal impacts.
Known limitations, recommended use patterns, warnings about edge cases.
Model cards, system cards, and datasheets are LIVING DOCUMENTS — they must be updated as models, systems, and datasets evolve. A model card written once at launch and never updated is a common audit finding.