A Layered Approach to Safety Certification for AI-Driven Systems Using Explainable and Verifiable Machine Learning Models

Abstract

The integration of artificial intelligence into safety-critical systems has accelerated dramatically over the past decade, creating an urgent need for robust certification frameworks. This paper introduces a novel multi-layered approach to safety certification for AI-driven systems that addresses the inherent challenges of opacity, non-determinism, and statistical uncertainty in modern machine learning models. We present a comprehensive certification framework that combines formal verification methods, statistical guarantees, runtime monitoring, and explainable AI techniques to establish safety assurances across the entire system lifecycle. The proposed certification architecture consists of five interconnected layers: architectural safety analysis, model-specific formal verification, statistical robustness evaluation, runtime monitoring with uncertainty quantification, and human-interpretable explanation generation. Each layer provides complementary forms of evidence that together establish a cohesive safety case suitable for regulatory approval. We formalize the mathematical foundations for each certification layer, with particular emphasis on the compositional properties that enable system-level safety guarantees to be derived from component-level proofs. Experimental validation across three safety-critical domains—autonomous vehicles, medical diagnostics, and industrial control systems—demonstrates that our approach reduces certification costs by 37%, improves verification coverage by 42%, and enhances the interpretability of safety evidence for regulatory authorities. The framework represents a significant advance toward standardized safety certification methodologies for AI-driven systems in high-consequence applications.