AI-Enabled Autonomous Systems Cannot Be Formally Verified for Safety

Autonomous cyber-physical systems — self-driving vehicles, surgical robots, autonomous drones, smart grid controllers — increasingly rely on deep neural networks for perception and decision-making, but there is no way to formally verify that these learning-enabled components will behave safely across all operating conditions. Traditional formal verification cannot handle neural networks because the networks have billions of parameters (making exhaustive analysis intractable), there are no widely accepted mathematical specifications for what "correct behavior" means for a neural network, and the networks are sensitive to slight input perturbations (adversarial examples) in ways that resist formal characterization. Traditional testing cannot provide safety guarantees either, because the input space is effectively infinite. This creates a certification deadlock: AI systems are increasingly capable but fundamentally unverifiable.

No autonomous system can be deployed in safety-critical applications — transportation, healthcare, defense, infrastructure — without assurance of safe behavior. The FAA, FDA, and DoD all require verification and validation pathways that do not exist for learning-enabled components. Autonomous vehicle crashes, medical AI misdiagnoses, and drone incidents have occurred precisely because of failures in learning-enabled perception and decision-making. As AI capabilities advance, the gap between what autonomous systems can do and what we can certify them to do safely is growing, potentially blocking deployment of transformative technologies indefinitely.

Exhaustive testing is infeasible for high-dimensional input spaces. Simulation-based validation covers limited scenarios and cannot guarantee coverage. Existing formal verification tools can handle small neural networks (tens of neurons) but do not scale to production systems with millions or billions of parameters. Robustness certification methods (verified bounds on adversarial perturbation) address a narrow aspect of safety and do not compose into system-level guarantees. Runtime monitoring can catch some failures during operation but provides no proactive assurance and cannot prevent unsafe actions before they occur. The traditional separation of development-time assurance from runtime assurance yields fragmented safety arguments that cannot adapt to systems that learn and evolve post-deployment.

Scalable formal verification methods specifically designed for neural network architectures; compositional verification approaches that prove properties of subsystems and compose them into system-level guarantees; specification languages that express safety requirements for learning-enabled perception in mathematically precise terms; continuous assurance frameworks that maintain safety arguments across the full lifecycle of learning, adapting autonomous systems; and hybrid approaches that combine formal guarantees on critical decision boundaries with statistical assurance for less critical functions.

A student team could implement and compare existing neural network verification tools (e.g., Marabou, alpha-beta-CROWN, nnenum) on a small autonomous decision-making task (e.g., a simplified lane-keeping controller), benchmarking scalability limits and identifying where current tools break down. This is a feasible software engineering/formal methods project. Alternatively, a team could design a runtime monitoring system for a specific autonomous behavior (e.g., obstacle avoidance) that detects when the neural network's inputs fall outside its verified operating envelope and triggers a safe fallback controller.