CS5720 - Week 13
Slide 249 of 260

Transparency and Accountability

Transparency Methods

AI Transparency involves making AI systems understandable and their decision-making processes clear to users, stakeholders, and affected parties.
  • 🔍
    Explainability Techniques
    Making AI decisions interpretable and understandable
  • 📄
    Model Documentation
    Creating comprehensive model cards and datasheets
  • 💬
    Stakeholder Communication
    Clear messaging about AI capabilities and limitations
  • 📊
    Performance Monitoring
    Continuous tracking and reporting of AI behavior

Accountability Framework

  • 🏛️
    Governance Structure
    Establishing clear roles and responsibilities
  • 👁️
    Oversight Mechanisms
    Review boards and audit processes
  • 🔧
    Remediation Processes
    Addressing issues and harm mitigation
  • 📈
    Reporting Standards
    Regular transparency reports and metrics

Model Card Example

Image Classification Model v2.1
Model Details
Architecture, training data, version info
Intended Use
Primary applications and use cases
Performance Metrics
Accuracy, fairness, robustness measures
Limitations
Known issues and failure modes
Ethical Considerations
Bias analysis and mitigation strategies
Version History
Changes and improvements over time
Transparency Dashboard
Real-time monitoring and reporting interface
Explainability API
Programmatic access to model explanations
Automated Reports
Regular stakeholder updates and metrics
← Previous Next →
Prepared by Dr. Gorkem Kar