AI Evaluation Metrics - Explainability

Definition:
Explainability refers to the AI model’s ability to provide clear, understandable reasons for its decisions or recommendations, making outputs transparent to users and auditors.

Guide for Compliance Team and Engineers:

Purpose:
Ensure users and regulators can trust and understand the AI’s health advice and product recommendations, enabling accountability and informed decision-making.

For Compliance Team:

  • Define Explainability Requirements: Establish what level of explanation is needed based on user audience and regulatory guidelines (e.g., simple explanations for retail users; detailed for pharmacies).

  • Documentation: Maintain comprehensive documentation of model logic, decision rules, and explanation methods.

  • User Communication: Ensure chatbot interfaces present explanations in accessible language without jargon.

  • Audit Compliance: Verify that explainability practices meet standards like GDPR’s “right to explanation” and FDA transparency guidelines.

  • Feedback Loop: Collect user and stakeholder feedback on clarity and usefulness of explanations to improve communication.

For Engineers:

  • Implement Explainability Methods: Use techniques such as SHAP, LIME, attention visualization, or counterfactual explanations to generate insights into model decisions.

  • Integrate Explanations in UI: Develop chatbot responses that include concise, relevant explanations alongside advice or recommendations.

  • Test Understandability: Conduct user testing to ensure explanations are clear and actionable.

  • Maintain Logs: Store explanation outputs linked to each decision for audit and troubleshooting purposes.

  • Balance Performance: Ensure explainability features do not significantly degrade model latency or accuracy.

AI Compliance, AI Evaluation MetricsFrancesca Tabor