What is Model Cards (AI Transparency)?
Model cards are structured documentation for machine learning models that provide transparency about a model's purpose, performance, limitations, and ethical considerations.
Model cards are structured documentation for machine learning models that provide transparency about a model's purpose, performance, limitations, and ethical considerations. Introduced by Mitchell et al. (Google, 2019), model cards are becoming a compliance requirement under the EU AI Act.
Model card contents: Model details (architecture, training data, intended use), Performance metrics (accuracy across different demographics, failure modes), Limitations (known biases, edge cases, out-of-distribution behavior), Ethical considerations (potential harms, mitigation strategies), and Maintenance (update frequency, versioning, responsible team).
Model cards serve multiple audiences: Regulators (compliance documentation), Users (understand model limitations), Developers (know when and how to use the model), and Society (transparency about AI systems that affect people).
Why It Matters
Model cards are evolving from best practice to legal requirement. The EU AI Act mandates transparency documentation for high-risk AI systems. Organizations that create model cards now are ahead of regulatory requirements.
Frequently Asked Questions
What is a model card?
Structured documentation for an ML model: purpose, performance, limitations, biases, and ethical considerations. Created by Google in 2019, increasingly required by regulation (EU AI Act).
Who should create model cards?
The team that trains/deploys the model. Include ML engineers (technical details), product managers (intended use), and ethics/legal teams (bias assessment, regulatory compliance). Update with each model version.
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