Domain 6: AI Transparency and Explainability

Assessment of explainable AI methodologies, decision documentation, and transparency mechanisms

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Domain Overview

AI Transparency and Explainability focuses on the organization's ability to understand, explain, and communicate how AI systems make decisions. This domain addresses explainable AI methodologies, decision process documentation, transparency in AI-human interactions, model output validation, communication of limitations, and regulatory compliance.

Transparency and explainability are critical for AI systems as they enable trust, facilitate regulatory compliance, support ethical use, and enable effective human oversight. Organizations must implement appropriate mechanisms to ensure AI systems are not "black boxes" but rather transparent and explainable to stakeholders, regulators, and users.

Assessment Areas

6.1 Explainable AI Methodologies

Evaluation of explainable AI methodologies implemented for high-risk AI applications, including local and global explanation techniques.

Key Control: NIST AI RMF (MAP function), ISO 42001 Section 8.4

Organizations should implement appropriate explainable AI methodologies based on use case requirements, including local and global explanation techniques.

6.2 AI Decision Process Documentation

Assessment of documentation practices for AI decision processes, including model logic, key features, and decision boundaries.

Key Control: ISO 42001 Section 7.5, NIST AI RMF (MEASURE function)

Organizations should develop and maintain detailed documentation of AI decision processes, including model logic, key features, and decision boundaries.

6.3 AI-Human Interaction Transparency

Evaluation of transparency mechanisms for AI-human interactions, including disclosure of AI use, confidence levels, and limitations.

Key Control: NIST AI RMF (MANAGE function), ISO 42001 Section 8.4

Organizations should implement transparency mechanisms for AI-human interactions, including disclosure of AI use, confidence levels, and limitations.

6.4 Model Output Interpretation and Validation

Assessment of methods for interpreting and validating model outputs, including statistical analysis, human review, and comparison with expected outcomes.

Key Control: NIST AI RMF (MEASURE function), ISO 42001 Section 9.1

Organizations should establish methods for interpreting and validating model outputs, including statistical analysis, human review, and comparison with expected outcomes.

6.5 Communication of AI System Limitations

Evaluation of communication practices regarding AI system limitations, including accuracy boundaries, known biases, and appropriate use cases.

Key Control: ISO 42001 Section 7.4, NIST AI RMF (GOVERN function)

Organizations should develop clear communication materials about AI system limitations, including accuracy boundaries, known biases, and appropriate use cases.

6.6 Algorithmic Transparency Compliance

Assessment of processes to identify and address algorithmic transparency requirements from applicable regulations, including documentation and reporting mechanisms.

Key Control: ISO 42001 Section 6.1, NIST CSF 2.0 (GOVERN function)

Organizations should establish processes to identify and address algorithmic transparency requirements from applicable regulations, including documentation and reporting mechanisms.

Compliance Considerations

Explainability Techniques

Various techniques can be used to enhance AI explainability:

  • Feature importance analysis (SHAP, LIME)
  • Counterfactual explanations
  • Rule extraction from complex models
  • Attention visualization for neural networks
  • Model-agnostic explanation methods
  • Inherently interpretable models (decision trees, linear models)

Regulatory Requirements

Several regulations include transparency and explainability requirements:

  • EU AI Act (risk-based transparency requirements)
  • GDPR Article 22 (right to explanation)
  • New York City Local Law 144 (automated employment decision tools)
  • FDA regulations for AI in medical devices
  • Financial sector regulations for algorithmic decision-making

Quick Assessment

Answer these key questions to quickly evaluate your AI transparency maturity:

Quick Assessment Result

Your organization appears to be at a basic level of AI transparency maturity.

Next steps: Implement basic explainable AI methodologies and document AI decision processes.

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Resources

Downloads

  • Transparency Domain Checklist
  • Full Assessment Package
  • Question Matrix

Related Domains

AI Model Development and Security AI Data Governance and Privacy AI Literacy and Training