Making Safe Use of AI in Safety-Critical Systems

Over the past few years, I’ve had a front-row seat to the growing excitement—and justified concern—around using artificial intelligence in safety-critical systems. Nowhere is this tension more visible than in aerospace, where the cost of failure is measured not just in money or mission delay, but in human lives. AI offers enormous potential, but in environments governed by strict certification, redundancy, and safety margins, “moving fast” is simply not an option.

In aerospace, we’ve always treated automation with healthy skepticism. Fly-by-wire systems, autopilots, and flight management computers took decades to earn trust, and only after exhaustive verification and validation. AI changes the game because it introduces behavior that is often probabilistic, data-dependent, and difficult to fully specify upfront. That doesn’t make AI unusable in safety-critical systems—but it does mean we must be far more deliberate about how and where we use it.

Understanding What “Safety-Critical” Really Means

One lesson that aerospace engineers learn early is that not all software is created equal. A system that recommends fuel-efficient routing is very different from one that influences flight control laws or collision avoidance. When I’ve worked on avionics-adjacent systems, the first question was never “Can we use AI here?” but rather “What happens if this fails?”

In safety-critical contexts, failure modes matter more than average performance. An AI model that performs at 99.9% accuracy may still be unacceptable if the remaining 0.1% leads to catastrophic outcomes. This mindset forces a shift away from typical AI success metrics and toward questions like predictability, bounded behavior, and graceful degradation.

Using AI Where It Adds Value—Without Taking Control

In my experience, the safest and most successful AI deployments in aerospace are advisory rather than authoritative. For example, AI can be extremely effective in:

• Predictive maintenance by analyzing sensor data to flag anomalies before components fail

• Decision support for pilots or ground crews, offering ranked recommendations rather than commands

• Simulation and design optimization, where AI explores large design spaces without touching live systems

In these cases, AI augments human decision-making instead of replacing it. The system can be powerful, but it never becomes the single point of failure. This “human-in-the-loop” approach aligns well with established aerospace safety principles and is far easier to justify during certification and audits.

Designing for Explainability and Traceability

One challenge I’ve repeatedly encountered is the discomfort regulators and safety engineers have with black-box models. In aerospace, we are expected to explain not just what a system does, but why it does it. When something goes wrong, there must be a clear chain of reasoning that can be inspected after the fact.

This has pushed many teams—including ones I’ve worked with—to favor simpler, more interpretable models over highly complex ones, even when the latter offer marginal performance gains. Explainability is not a “nice to have” in safety-critical systems; it is often the difference between an AI system being deployable or being rejected outright.

Equally important is traceability. Training data, model versions, parameter changes, and validation results must be documented as rigorously as traditional software requirements. Treating AI models as first-class configuration-controlled artifacts is essential if they are to coexist with certified systems.

Testing Beyond the Happy Path

Aerospace engineers are famously paranoid, and for good reason. When applying AI, that paranoia must extend to testing strategies. It’s not enough to validate models on clean, representative datasets. We must actively seek out edge cases, degraded sensor inputs, rare environmental conditions, and unexpected interactions with other systems.

In one project, I saw more value come from stress-testing an AI model with intentionally corrupted or incomplete data than from weeks of additional training. The goal wasn’t to make the model “perfect,” but to understand how it fails—and whether those failures remain within acceptable safety bounds.

Treating AI as a Living System

Unlike traditional avionics software, AI systems can drift over time as operational data changes. This reality clashes with the aerospace preference for frozen, certified baselines. The compromise I’ve seen work best is to separate learning from operation. Models are trained, updated, and validated offline under strict controls, then deployed as fixed artifacts with well-defined operating envelopes.

Continuous monitoring is also critical. If an AI system starts behaving outside its validated assumptions, it should be detected quickly and either flagged to operators or automatically reverted to a safe fallback mode. In aerospace terms, AI should fail passive or fail operational—never fail silently.

A Culture Shift, Not Just a Technical One

Perhaps the biggest takeaway from my experience is that safe AI adoption is as much about culture as it is about algorithms. Teams used to rapid experimentation must adapt to the discipline of safety engineering, while traditional safety teams need to develop a working understanding of machine learning’s strengths and limitations.

When these worlds collaborate early—rather than colliding late in the development cycle—the results are far better. AI stops being seen as a risky black box and starts becoming just another engineered component, subject to the same rigor as everything else that flies.

Closing Thoughts

AI absolutely has a role to play in safety-critical systems, including aerospace—but only if we resist the temptation to treat it like conventional software or consumer technology. By constraining its authority, demanding explainability, rigorously testing failure modes, and embedding it within established safety frameworks, we can unlock its benefits without compromising trust.

From where I stand, the future isn’t about AI replacing certified systems. It’s about AI earning its place among them, one carefully engineered step at a time.