Model Card
A model card is a short, structured document that describes an AI model's intended use, performance, limitations, training data, evaluation results across demographic and operational subgroups, and known failure modes — the AI equivalent of a nutrition label.
AIDOLS Research Team · Editorial standards
Full definition
Introduced by Mitchell et al. (Google, 2019), model cards are now the de-facto disclosure standard. Hugging Face renders each model repository's README as its model card and calls model cards essential, the EU AI Act mandates equivalent disclosures for high-risk systems, and Anthropic/OpenAI/Google publish model or system cards for major releases. A useful card includes: intended use, out-of-scope use, training data summary, evaluation metrics by subgroup, ethical considerations, and update history. AIDOLS includes a model card review in every governance engagement.
Why it matters
Without model cards, downstream users cannot make informed deployment decisions and regulators cannot audit. With them, AI procurement evolves from PDF marketing decks to comparable, auditable disclosures.
Example
A health-tech company evaluates three vision models for diabetic-retinopathy screening; only one publishes subgroup performance by skin tone, exposing a 9-point sensitivity gap that disqualifies the other two.
Next steps
Related terms
- AI Bill of Materials (AIBOM)An AI Bill of Materials (AIBOM) is a structured, machine-readable inventory of every component used in an AI system — base models, fine-tuning datasets, third-party APIs, prompts, vector indexes, libraries, and licenses — extending the SBOM concept to AI supply chains.
- Datasheet for DatasetsA datasheet for datasets is a structured document — proposed by Gebru et al. (2018) — describing a dataset's motivation, composition, collection process, labeling, preprocessing, recommended uses, distribution, and maintenance, so downstream model developers can make informed choices.
- AI AuditAn AI audit is a structured, evidence-based examination of an AI system or AI program against defined criteria — covering training data, model, deployment context, monitoring, and governance — performed by an internal team, an external firm, or a regulator.
- AI GovernanceAI governance is the framework of policies, roles, controls, and processes an organization uses to ensure its AI systems are lawful, safe, fair, accountable, and aligned with business intent — across the full lifecycle from problem framing to retirement.
More governance & risk terms
- AI Risk AssessmentAn AI risk assessment is a structured review of an AI system's potential harms — to individuals, groups, the organization, and society — covering likelihood, severity, affected populations, and mitigation controls across safety, fairness, security, privacy, and compliance dimensions.
- NIST AI RMFThe NIST AI Risk Management Framework (AI RMF 1.0, January 2023) is a voluntary U.S. framework for managing AI risks throughout the lifecycle, organized around four core functions — Govern, Map, Measure, Manage — and seven characteristics of trustworthy AI.
Source & further reading
Primary source: Mitchell et al. — "Model Cards for Model Reporting" (FAT*) (2019).
Citation policy: this entry is part of the AIDOLS AI Implementation Glossary and may be quoted for research, journalism, and education with attribution to aidolsgroup.com/en/glossary/model-card/.