Artificial Intelligence (AI) has transformed the world of technology, enabling systems to learn, adapt, and make decisions without explicit programming. From autonomous vehicles to medical diagnostics and flight control systems, AI promises unprecedented efficiency and capability. However, when it comes to safety-critical systems—where failure could result in injury, loss of life, or significant damage—the use of AI introduces profound challenges that go far beyond traditional software engineering. Unlike conventional software, which behaves predictably according to its programmed logic, AI is built on learning and training. Its decisions and outputs depend heavily on the data it has been trained on and the patterns it recognizes during runtime. This adaptive, data-driven behavior means that an AI system’s responses may vary with changing inputs or environments, often in ways that are not explicitly defined or foreseen by developers. While this flexibility is a strength in many applica...
In aerospace software development, analysis is a virtue. DO-178C demands discipline in planning, requirements definition, and verification, and that discipline has saved countless systems from unsafe behavior. But over time, I’ve seen a subtle and dangerous pattern emerge in some programs—analysis paralysis. It doesn’t come from laziness or incompetence. It comes from teams trying very hard to “do DO-178C right.” Ironically, that effort can sometimes work against both safety and schedule.