AI Compliance Framework

Overview

Add a brief overview of this document here.

This document outlines the compliance requirements and guidelines for AI components in Anya Core.

Table of Contents

Regulatory Framework

1. Global Regulations

  • GDPR (EU):
  • Right to explanation for automated decisions
  • Data subject rights

  • Data protection by design and by default

  • CCPA/CPRA (California):

  • Right to opt-out of AI processing
  • Data access and deletion rights

  • Non-discrimination provisions

  • AI Act (EU):

  • Risk-based classification of AI systems
  • Prohibited AI practices
  • Transparency requirements

2. Industry Standards

  • ISO/IEC 42001 - AI Management System
  • NIST AI RMF - AI Risk Management Framework
  • IEEE 7000 - Ethical Considerations in AI

Data Protection

1. Data Collection

  • Purpose Limitation: Clearly define and document the purpose of data collection
  • Data Minimization: Collect only necessary data for the intended purpose
  • Consent Management: Implement robust consent collection and management

2. Data Processing

  • Anonymization: Apply appropriate anonymization techniques
  • Encryption: Encrypt data at rest and in transit
  • Access Control: Implement role-based access control (RBAC)

3. Data Retention

  • Define and enforce data retention policies
  • Implement secure data deletion procedures
  • Document data lifecycle management

Model Governance

1. Model Development

  • Version Control: Maintain version control for all models
  • Reproducibility: Ensure experiments are reproducible
  • Documentation: Document model architecture, training data, and hyperparameters

2. Model Deployment

  • Validation: Validate models before deployment
  • Monitoring: Implement continuous monitoring of model performance
  • Rollback: Maintain ability to rollback to previous versions

3. Model Risk Management

  • Bias and Fairness: Regularly assess models for bias
  • Robustness: Test models against adversarial attacks
  • Explainability: Ensure model decisions can be explained

Ethical AI

1. Fairness

  • Bias Detection: Implement tools to detect and mitigate bias
  • Fairness Metrics: Define and track fairness metrics
  • Impact Assessment: Conduct regular impact assessments

2. Transparency

  • Documentation: Maintain comprehensive documentation
  • Disclosure: Clearly disclose AI usage to users
  • Explainability: Provide explanations for AI decisions

3. Accountability

  • Responsibility: Assign clear ownership of AI systems
  • Oversight: Establish governance structures
  • Audit: Conduct regular audits of AI systems

Documentation Requirements

1. Model Documentation

  • Model Card: Create a model card for each model
  • Data Sheet: Document the training dataset
  • Technical Specifications: Detail model architecture and parameters

2. Process Documentation

  • Development Process: Document the development lifecycle
  • Testing Procedures: Detail testing methodologies
  • Deployment Process: Document deployment procedures

3. Compliance Documentation

  • Risk Assessments: Document risk assessments
  • Impact Assessments: Maintain records of impact assessments
  • Incident Reports: Document any incidents and remediation

Testing and Validation

1. Pre-deployment Testing

  • Unit Testing: Test individual components
  • Integration Testing: Test component interactions
  • System Testing: Test the complete system

2. Ongoing Validation

  • Performance Monitoring: Continuously monitor model performance
  • Drift Detection: Detect concept and data drift
  • A/B Testing: Compare model versions

3. Adversarial Testing

  • Penetration Testing: Test for security vulnerabilities
  • Red Teaming: Simulate attacks on the system
  • Bias Testing: Test for discriminatory outcomes

Incident Response

1. Incident Classification

  • Severity Levels: Define incident severity levels
  • Response Times: Set response time targets
  • Escalation Paths: Define escalation procedures

2. Response Process

  • Containment: Contain the incident
  • Eradication: Remove the threat
  • Recovery: Restore normal operations
  • Post-mortem: Analyze and learn from the incident

3. Reporting

  • Internal Reporting: Report incidents internally
  • Regulatory Reporting: Report to regulators as required
  • User Notification: Notify affected users

Audit Trails

1. Data Logging

  • Access Logs: Log all data access
  • Model Logs: Log model inputs and outputs
  • Decision Logs: Log AI decisions

2. Audit Requirements

  • Retention Period: Define log retention periods
  • Access Control: Control access to audit logs
  • Integrity: Ensure log integrity

3. Regular Audits

  • Internal Audits: Conduct regular internal audits
  • External Audits: Engage third-party auditors
  • Remediation: Address audit findings

Compliance Checklist

1. Data Protection

  • [ ] Data protection impact assessments conducted
  • [ ] Data minimization principles followed
  • [ ] Consent management system in place

2. Model Development

  • [ ] Model version control implemented
  • [ ] Training data documented
  • [ ] Bias testing conducted

3. Deployment

  • [ ] Model validation completed
  • [ ] Monitoring systems in place
  • [ ] Rollback procedures tested

4. Documentation

  • [ ] Model cards created
  • [ ] Process documentation complete
  • [ ] Compliance records maintained

5. Testing

  • [ ] Pre-deployment testing completed
  • [ ] Ongoing validation in place
  • [ ] Adversarial testing conducted

6. Incident Response

  • [ ] Incident response plan in place
  • [ ] Team trained on procedures
  • [ ] Reporting mechanisms established

7. Audit

  • [ ] Audit trails implemented
  • [ ] Regular audits scheduled
  • [ ] Audit findings addressed

Implementation Guidelines

1. Technical Implementation

  • Logging: Implement comprehensive logging
  • Monitoring: Set up monitoring systems
  • Automation: Automate compliance checks

2. Organizational Implementation

  • Training: Train staff on compliance requirements
  • Roles: Define compliance roles and responsibilities
  • Culture: Foster a culture of compliance

3. Continuous Improvement

  • Review: Regularly review compliance measures
  • Update: Update procedures as needed
  • Feedback: Incorporate feedback from audits and incidents

Templates

Model Card Template

# Model Card

## Model Details
- **Name**: 
- **Version**: 
- **Date**: 
- **Owners**: 
- **License**: 

## Intended Use
- **Primary Use Case**: 
- **Intended Users**: 
- **Out of Scope Uses**: 

## Training Data
- **Datasets**: 
- **Preprocessing**: 
- **Labeling**: 

## Evaluation
- **Metrics**: 
- **Results**: 
- **Limitations**: 

## Ethical Considerations
- **Bias**: 
- **Fairness**: 
- **Impact**:

Data Sheet Template

# Dataset Card

## Dataset Details
- **Name**: 
- **Description**: 
- **Creation Date**: 
- **Maintainers**: 

## Composition
- **Instances**: 
- **Features**: 
- **Splits**: 

## Collection
- **Source**: 
- **Sampling**: 
- **Time Period**: 

## Preprocessing
- **Cleaning**: 
- **Transformation**: 
- **Labeling**: 

## Distribution
- **Format**: 
- **Access**: 
- **License**: 

## Maintenance
- **Updates**: 
- **Contact**: 
- **Errata**:

Review and Update

This document should be reviewed and updated at least annually or when significant changes occur in:

  • Regulatory requirements
  • Organizational structure
  • Technology stack
  • Risk profile

Approval

Role Name Signature Date
AI Ethics Officer
Data Protection Officer
CTO

Version History

Version Date Changes Author
1.0 2025-03-20 Initial version AI Team

References

  1. EU AI Act
  2. GDPR
  3. NIST AI RMF
  4. ISO/IEC 42001
  5. IEEE 7000

[AIR-3][AIS-3][BPC-3][RES-3]

This document is a living document and should be updated as needed to reflect changes in regulations, technology, and organizational requirements.

See Also