Model Provenance Guide

What is Model Provenance?

Model provenance is the complete record of a model's lifecycle, including its data sources, training process, modifications, deployments, and performance history. It provides transparency, accountability, and traceability for AI systems.

Why Model Provenance Matters

Model provenance is essential for:

• Compliance: Meeting regulatory requirements for AI transparency
• Auditability: Enabling internal and external audits
• Reproducibility: Ensuring models can be recreated and validated
• Trust: Building confidence in AI systems
• Debugging: Identifying issues when models underperform

Components of Model Provenance

Data Lineage

Track the complete journey of your training data:

• Data sources and collection methods
• Data preprocessing steps and transformations
• Feature engineering and selection
• Data quality checks and validation
• Data versioning and snapshots

Model Lineage

Record the model development process:

• Model architecture and hyperparameters
• Training configuration and environment
• Training metrics and performance
• Model versions and iterations
• Validation and testing results

Deployment History

Track model deployments and updates:

• Deployment environments and configurations
• Performance monitoring and metrics
• Model updates and rollbacks
• Incident reports and resolutions
• Retirement and archival

Digital Signatures

Fairmind provides digital signature capabilities to ensure model authenticity and integrity:

What are Digital Signatures?

Digital signatures use cryptographic techniques to verify that a model hasn't been tampered with and to confirm its origin. They provide:

• Authenticity: Confirms the model was created by the claimed author
• Integrity: Ensures the model hasn't been modified since signing
• Non-repudiation: Prevents the author from denying they created the model

How Digital Signatures Work

The digital signature process involves:

1. Hash Generation: Creating a unique hash of the model file
2. Signing: Encrypting the hash with a private key
3. Verification: Decrypting with the public key to verify authenticity

Getting Started with Model Provenance

Step 1: Register Your Model

Start by registering your model in the Fairmind platform:

• Upload your model file
• Provide model metadata (name, version, description)
• Specify the model type and framework
• Add tags and categories

Step 2: Document Data Sources

Document all data sources used in training:

• Data source URLs or locations
• Data collection dates and methods
• Data preprocessing steps
• Data quality assessments

Step 3: Record Training Process

Document the training process:

• Training configuration and hyperparameters
• Training environment details
• Training metrics and logs
• Validation results

Step 4: Generate Digital Signature

Create a digital signature for your model:

• Generate a cryptographic key pair
• Sign the model with your private key
• Store the signature securely
• Share the public key for verification

Model Cards

Model cards provide standardized documentation for AI models:

Model Card Components

• Model Details: Name, version, type, and framework
• Intended Use: Purpose and intended applications
• Training Data: Data sources, preprocessing, and quality
• Training Process: Architecture, hyperparameters, and metrics
• Evaluation: Performance metrics and bias analysis
• Limitations: Known limitations and potential risks

Audit Trails

Fairmind maintains comprehensive audit trails for all model activities:

What's Tracked

• Model creation and registration
• Training runs and experiments
• Model deployments and updates
• Performance monitoring and alerts
• User access and modifications
• Compliance checks and validations

Audit Trail Features

• Immutable Logs: Audit trails cannot be modified or deleted
• Timestamped Events: All events include precise timestamps
• User Attribution: All actions are attributed to specific users
• Search and Filter: Easy searching and filtering of audit events
• Export Capabilities: Export audit trails for external review

Compliance and Regulations

Model provenance helps meet various regulatory requirements:

GDPR Compliance

For models processing personal data, provenance helps demonstrate:

• Lawful basis for processing
• Data minimization and purpose limitation
• Right to explanation for automated decisions
• Data protection impact assessments

AI Act Compliance

For high-risk AI systems, provenance supports:

• Risk management and mitigation
• Quality management systems
• Transparency and documentation
• Human oversight and control

Best Practices

• Document everything from the start of your model development
• Use consistent naming conventions for models and versions
• Regularly update model cards with new information
• Implement automated provenance tracking where possible
• Regularly review and validate your provenance records
• Train your team on provenance best practices

Next Steps

Continue your AI governance journey with these related guides: