Taming Silent Failures: A Framework for Verifiable AI Reliability

Silent failures pose a unique AI safety risk: Unlike traditional system failures that trigger alerts, AI can confidently produce wrong answers that go undetected—a particular danger in safety-critical applications like autonomous vehicles. FAME addresses this by combining formal verification and real-time monitoring to catch these dangerous errors.

FAME achieved 93.5% detection of critical safety violations: In testing on autonomous vehicle perception systems, the framework successfully identified critical safety problems that would otherwise have remained hidden, demonstrating practical effectiveness in real-world applications.

The framework aligns with existing safety standards: By contextualizing FAME within ISO 26262 and ISO/PAS 8800 standards, the paper provides a pathway for organizations to deploy AI systems that meet established regulatory requirements for safety-critical systems, making trustworthy AI deployment more feasible.

Verification requires both mathematical rigor and continuous monitoring: FAME combines offline formal synthesis (mathematical proof of safety properties) with online runtime monitoring (real-time checking during operation), suggesting that responsible AI deployment needs multiple layers of oversight rather than relying on any single approach.

The shift from probabilistic to provable safety is essential: Rather than accepting that AI systems will occasionally fail, this framework represents a move toward enforcing demonstrable safety guarantees—a fundamental change in how we should think about deploying AI in high-stakes environments.