DataTerminology.comNEW
A standardized document that details an ML model’s purpose, data, performance, evaluation context, limitations, and ethical considerations.
DESCRIPTION
Model cards are transparency artifacts that accompany trained models. They summarize intended use, key metrics, datasets, evaluation setups, known limitations, and ethical risks. They help stakeholders understand appropriateness, failure modes, and responsible deployment contexts. Model cards support governance, audits, and risk reviews, and can be versioned alongside model releases. They complement system-level documentation and data datasheets to create a complete lineage and accountability record.
ALTERNATIVE DEFINITIONS
Model Cards for Model Reporting
We propose model cards that accompany trained machine learning models and provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups.
Introduces model cards to communicate evaluations across diverse conditions for transparency and accountability.
Google AI Blog
Model Cards are short documents that accompany trained machine learning models to provide benchmarked evaluation and other relevant information to foster greater transparency and accountability.
Highlights brevity, benchmarking, and accountability aims.
RELATED TERMS
Datasheet for Datasets Model Registry Explainability Fairness Documentation AI Governance AI Risk Management Model Risk Management
COMPETING TERMS
AI FactSheets System Card
KNOWLEDGE AREAS
AI Governance MLOps Data Science AI
TARGET AUDIENCE
AI Engineer MLOps Engineer AI Product Manager Data Scientist Data Governance Manager Chief Data Officer
EXTERNAL SOURCES
- Wikipedia - Model card
- ArXiv - Seminal model cards paper
- Google - Model Card Toolkit and guidance
- IBM - Related AI factsheets concept
TOP QUESTIONS
What information should a Model Card include?
Purpose, intended use, datasets, pre-processing, evaluation metrics, test conditions, limitations, ethical considerations, and versioning details are commonly included.
Category: Requirements
How do Model Cards help Responsible AI?
They make assumptions and limitations explicit, enabling better risk assessments, stakeholder communication, and governance audits.
Category: Benefits
Who writes a Model Card?
Model developers collaborate with product, risk, and domain experts; reviewers validate accuracy and completeness before release.
Category: Ownership
What tools support Model Cards?
Google’s Model Card Toolkit and custom templates integrated into MLOps pipelines streamline creation and publishing.
Category: Tooling
When should a Model Card be updated?
On major model changes, data updates, new evaluations, detected incidents, or when expanding to new contexts.
Category: Lifecycle
How do Model Cards relate to System Cards?
Model cards document individual models, while system cards describe end-to-end systems that may include multiple models and components.
Category: Relation
What are common Model Card pitfalls?
Vague intended use, missing context-specific metrics, and lack of traceability to data and training details reduce usefulness.
Category: Mistakes
Are Model Cards mandatory?
Not universally, but increasingly encouraged by governance frameworks and customer expectations for transparency.
Category: Compliance
How detailed should performance metrics be in a Model Card?
Include metrics disaggregated across relevant subpopulations and deployment conditions to reveal context-specific behavior.
Category: BestPractice