Global Avionics Round-Up from Aircraft Value News (AVN)
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Artificial intelligence (AI) already is flying aboard commercial aircraft, even if it is rarely advertised as such. Algorithms optimize flight paths, flag maintenance issues, tune engine performance, and assist pilots with increasingly complex information management.
The promise is compelling. AI can reduce workload, improve safety margins, and extract value from oceans of operational data that human crews could never process in real time. Yet aviation has never been about blind faith in technology. It is about trust that has been earned, documented, tested, and re-tested.
As AI pushes deeper into avionics, the central question is not whether it can perform. It is whether it can be trusted.
Trustworthiness in avionics is not a vague philosophical concept. It is a concrete blend of predictability, explainability, reliability, and accountability. Traditional avionics earned trust through deterministic behavior.
Given a specific input, the system produced a known output every time. Engineers could trace logic paths, certify failure modes, and prove compliance with standards down to the line of code. AI, especially machine learning based systems, breaks that mold. It learns from data rather than following fixed rules. That difference is both its strength and its greatest obstacle to acceptance in the cockpit.
The Dangers of the “Black Box”
One of the core challenges is explainability. Pilots and regulators do not need to understand every mathematical detail, but they do need to know why a system made a recommendation or took an action.
In avionics, unexplained behavior is unacceptable. If an AI-based flight management function suggests a reroute or a performance adjustment, crews must be able to understand the reasoning quickly and confidently.
Black box models that can’t provide human readable explanations struggle to meet that bar. As a result, explainable AI has become a major focus in aviation research, even if it lags behind more consumer facing AI applications.
Another pillar of trust is predictability across edge cases. Aviation systems are certified not just for normal operations, but for abnormal and rare scenarios. Weather anomalies, sensor failures, unexpected traffic conflicts, and human factors all collide in real world operations.
AI systems trained on historical data can perform brilliantly in familiar conditions and falter when confronted with something novel. In aviation, novelty is not an excuse. Trustworthy AI must demonstrate graceful degradation. It must know when it does not know and hand control back to deterministic systems or human operators without drama.
Certification frameworks are now wrestling with these realities. Existing standards such as DO 178C were built for conventional software. They assume requirements are defined upfront and code is written to meet them. Machine learning systems invert that logic.
The behavior emerges from training data, not explicit requirements. Regulators are responding cautiously, and for good reason. Trust in avionics is cumulative. It is built through incremental deployment, limited authority, and extensive operational monitoring. Early AI applications are therefore clustered in advisory roles rather than command roles. They suggest, predict, and alert, but they do not decide alone.
Human factors sit at the center of this trust equation. Pilots must neither over trust nor under trust AI systems. Over trust can lead to complacency and skill fade. Under trust can result in ignored alerts and lost benefits.
Achieving the right balance requires careful interface design and training. If AI recommendations are consistently accurate and presented clearly, pilots learn to rely on them appropriately. If they are opaque or occasionally erratic, trust erodes quickly. In aviation, trust lost is far harder to regain than trust earned slowly.
The Role of Cybersecurity
Cybersecurity further complicates the picture. AI systems can be targets for manipulation through data poisoning or adversarial inputs. In avionics, where connectivity is increasing through satellite links and ground systems, the attack surface is expanding.
Trustworthiness therefore includes resilience against intentional interference. Secure architectures, isolation, and rigorous verification are as essential to AI as they are to any flight critical system.
There is also an economic dimension to trust. Airlines and lessors care deeply about residual values and certification longevity. An avionics suite that relies heavily on opaque AI may face slower regulatory approval or limited operational envelopes, affecting aircraft value.
Conversely, systems that integrate AI in a transparent, certifiable manner can enhance efficiency without raising red flags. Trustworthiness becomes a market differentiator, not just a safety concern.
This article originally appeared in Aircraft Value News.
John Persinos is the editor-in-chief of Aircraft Value News.