Model Cards

Transparency Fairness Safety

Description

Model cards are standardised documentation frameworks that systematically document machine learning models through structured templates. The templates cover intended use cases, performance metrics across different demographic groups and operating conditions, training data characteristics, evaluation procedures, limitations, and ethical considerations. They serve as comprehensive technical specifications that enable informed model selection, prevent inappropriate deployment, support regulatory compliance, and facilitate fair assessment by providing transparent reporting of model capabilities and constraints across diverse populations and scenarios.

Example Use Cases

Safety

Documenting a medical diagnosis AI with detailed performance metrics across different patient demographics, age groups, and clinical conditions, enabling healthcare providers to understand when the model should be trusted and when additional expert consultation is needed for patient safety.

Fairness

Creating comprehensive model cards for hiring algorithms that transparently report performance differences across demographic groups, helping HR departments identify potential bias issues and ensure equitable candidate evaluation processes.

Transparency

Publishing detailed model documentation for a credit scoring API that clearly describes training data sources, evaluation methodologies, and performance limitations, enabling financial institutions to make informed decisions about model deployment and regulatory compliance.

Limitations

Creating comprehensive model cards requires substantial time, expertise, and resources to gather performance data across diverse conditions and demographic groups, potentially delaying model deployment timelines.
Information can become outdated quickly as models are retrained, updated, or deployed in new contexts, requiring ongoing maintenance and version control to remain accurate and useful.
Organisations may provide incomplete or superficial documentation to avoid revealing competitive advantages or potential liabilities, undermining the transparency goals of model cards.
Lack of standardised formats and enforcement mechanisms means model card quality and completeness vary significantly across different organisations and use cases.
Technical complexity of documenting model behaviour across all relevant dimensions may exceed the expertise of some development teams, leading to gaps in critical information.

Resources

Model Cards for Model Reporting

Research Paper•Margaret Mitchell et al.•Oct 5, 2018

Foundational paper introducing model cards as a framework for transparent model reporting and responsible AI documentation

Model Card Guidebook

Tutorial

Comprehensive guide providing updated model card templates, creator tools, and practical insights for implementing model documentation across diverse stakeholder needs

scikit-learn model cards documentation

Tutorial

Practical tutorial demonstrating how to create comprehensive model cards for scikit-learn models using the skops library with metrics, visualisations, and metadata

Related Techniques

Name	Description	Assurance Goals
MLflow Experiment Tracking	MLflow is an open-source platform that tracks machine learning experiments by automatically logging parameters, metrics, models, and artifacts throughout the ML lifecycle. It provides a centralised repository for comparing different experimental runs, reproducing results, and managing model versions. Teams can track hyperparameters, evaluation metrics, model files, and execution environment details, creating a comprehensive audit trail that supports collaboration, reproducibility, and regulatory compliance across the entire machine learning development process.	Transparency Reliability
ANCHOR	ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of feature conditions that guarantee a specific prediction with high confidence. It searches for 'anchor' conditions (e.g., 'age > 30 AND income < £50k') that ensure the model gives the same prediction at least 95% of the time when those conditions are met. This creates human-readable rules that users can trust as sufficient conditions for understanding why a particular decision was made.	Explainability Transparency
SHapley Additive exPlanations	SHAP explains model predictions by quantifying how much each input feature contributes to the outcome. It assigns an importance score to every feature, indicating whether it pushes the prediction towards or away from the average. The method systematically evaluates how predictions change as features are included or excluded, drawing on game theory concepts to ensure a fair distribution of contributions.	Explainability Fairness Reliability
Federated Learning	Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or data centres) without requiring centralised data sharing. Participants train models locally on their private datasets and only share model updates (gradients, weights, or aggregated statistics) with a central coordinator. This distributed approach serves multiple purposes: preserving data privacy and sovereignty, reducing communication costs, enabling learning from diverse data sources, improving model robustness through heterogeneous training, and facilitating compliance with data protection regulations whilst maintaining model performance comparable to centralised training.	Privacy Reliability Safety Fairness
Coefficient Magnitudes (in Linear Models)	Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients. Features with larger absolute coefficients are considered to have a stronger impact on the prediction, while the sign of the coefficient indicates the direction of that influence (positive or negative). This technique provides a straightforward and transparent way to understand the direct linear relationship between each input feature and the model's output.	Explainability Transparency
Gradient-weighted Class Activation Mapping	Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making a specific classification. Unlike pixel-level techniques, Grad-CAM produces coarser region-based explanations by using gradients from the predicted class to weight the CNN's final feature maps, then projecting these weighted activations back to create an overlay on the original image. This provides intuitive visual explanations of where the model is 'looking' for evidence of different classes.	Explainability Fairness

Tags

Applicable Models:

Assurance Goal Category:

Data Requirements:

Access To Training Data

Sensitive Attributes

Data Type:

Evidence Type:

Expertise Needed:

Regulatory Compliance

Lifecycle Stage:

Model Development

System Deployment And Use

Technique Type: