AI in Aviation: Managing Risk You Can't See Yet

I enhanced my mathematical crash causality model with AI capabilities in 2023. Not because it was trendy, but because AI reveals risk patterns that human analysis alone cannot surface at scale. But that same power introduces failure modes we've never encountered.

The AI Risk Paradox

AI systems promise to reduce risk by identifying patterns faster and more accurately than humans. And they do. But they also introduce uncertainty that traditional safety frameworks weren't built to assess.

What Makes AI Risk Different?

Traditional risk management assumes:

• Known failure modes — You can list what might go wrong
• Explainable decisions — You can trace why something happened
• Static systems — The system behaves the same way every time

AI violates all three assumptions.

The Three AI Risk Categories Leaders Must Address

1. Model Risk: When the AI Is Wrong (And You Don't Know It)

AI models are trained on historical data. But aviation operations encounter scenarios that don't exist in the training set. When this happens:

• The model still produces an output (it has to)
• The output may look confident (but confidence ≠ accuracy)
• You won't know it's wrong until something breaks

The question: How do you validate AI predictions when ground truth is unavailable or delayed?

2. Decision Authority: Who's Really in Control?

AI-assisted decision-making creates a dangerous ambiguity: Is the human overriding the AI, or is the AI overriding the human?

Consider predictive maintenance recommendations:

• AI says: "Delay this inspection 48 hours, confidence 92%"
• Mechanic has a gut feeling something's wrong
• What's the protocol? Who has final authority?

If you haven't defined this before the scenario occurs, you're creating liability exposure.

3. Systemic Dependency: When Operations Can't Function Without AI

The most insidious AI risk: your operation becomes dependent on systems you don't fully understand.

What happens when:

• The AI model goes offline for maintenance?
• A critical update changes model behavior?
• The vendor discontinues support?

Do you have manual fallback procedures? Are they tested? Or has institutional knowledge already atrophied because "the AI handles that"?

How to Manage AI Risk Without Blocking Innovation

You can't eliminate AI risk. But you can make it visible, quantifiable, and manageable.

The Leadership Question

If an AI-assisted decision leads to an incident, can you explain to regulators—and your board—how you validated that the system was safe to deploy?

If the answer is "we trusted the vendor" or "it worked in testing," you have an exposure gap.

AI is a force multiplier for safety—when managed correctly. But it introduces risk patterns that traditional frameworks don't surface. The leaders who get this right won't be the ones who avoid AI. They'll be the ones who saw the risk early and built the frameworks to manage it.