Explainable AI Audit

Technical Explanation

When autonomous 6G AI models make critical decisions—such as disconnecting a flight-critical drone, dropping a network slice, or rerouting emergency 911 traffic—telecom regulators legally mandate explainability. The Explainable AI (XAI) Audit engine generates mathematical feature-attribution scores (using SHAP, Integrated Gradients, and causal graphs), producing auditable natural-language explanations proving that the AI's decision was unbiased, lawful, and necessary.

Key Functions

• Generating auditable mathematical explanations for autonomous AI network decisions
• Feature attribution scoring (SHAP values) identifying which inputs triggered AI actions
• Providing legally certified audit trails for regulatory compliance and dispute resolution
• Detecting hidden algorithmic bias or model drift before catastrophic network failures occur
• Essential compliance layer for ITU-R IMT-2030 ethical and trustworthy AI mandates