Model Card
What is Model Card?
A documentation framework for machine learning models that provides details about a model's intended use, performance metrics, training data, limitations, and ethical considerations. Model cards promote transparency and responsible AI use.
Terms that appear alongside model card
Each of these is named in at least one of the same controls as model card. The number is how many controls name both.
- nist 4 shared controls
- red team 3 shared controls
- eu ai act 3 shared controls
- stress testing 2 shared controls
- incident reporting 2 shared controls
- sandboxing 2 shared controls
- content filtering 2 shared controls
- policy 2 shared controls
Frameworks that govern model card
What the standards actually require on model card
Requirements naming model card across 6 standards, quoted from the control text.
Developers should adopt or develop clear, consistent reporting formats, such as data sheets, model cards or system cards, to communicate risk management outcomes, including residual risk, to relevant stakeholders.
australia-guidance-for-ai-adoption-2025::3.1.3 · 3.1.3 Report risk outcomes in consistent formats →The developer must make available, to the extent feasible, documentation through artifacts such as model cards, dataset cards or impact assessments necessary for the deployer (or its third party) to complete an impact assessment under 6-1-1703(3).
CO-AIA-1702-3 · Impact-Assessment Support Artifacts →Security Dimension 6 Data Integrity per X.805 Clause 6.6: Data Integrity keeps data correct and accurate. Data are shielded from being altered, deleted, created or copied without authorization, and any such unauthorized activity is made detectable.
X805-Dim6-Data-Integrity-Correctness-Accuracy-Unauthorized-Modification-Deletion-Detection · ITU-T X.805 Security Dimension 6 - Data Integrity + Correctness + Accuracy + Unauthorized Modification Prevention + Deletion Detection + Hashing + HMAC + Digital Signatures + Merkle Trees + Blockchain Integrity + File Integrity Monitoring (FIM) →Adhere to OECD AI Principles Section 1.3 (Transparency and explainability). AI actors should commit to (a) transparency and responsible disclosure regarding AI systems to foster understanding of AI systems + make stakeholders aware of their interactions with A...
OECDAI-2 · Transparency, Explainability, and Public-Facing Disclosure →Enable transparency, explainability and traceability over data sourcing, datasets, processes and development decisions, human review and appeal (Box 2.8: document use and risk information, data sources and processing, code and reproducibility details, how outp...
oecd-due-diligence-guidance-for-responsible-ai-2026::3.1.G2-transp · 3.1.G2-transp Mitigation by developers: transparency, explainability and traceability →Operate LLM governance + inventory + risk + change management. Requirements include (a) maintain inventory of LLM systems + models + datasets + tools + agents + integrations + (b) maintain risk assessment + classification per LLM system aligned to applicable r...
OWASPLLM-7 · LLM Governance, Inventory, Risk and Change Management →Questions people ask about model card
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