We live in challenging times lately due to the pandemic; it is a fight for many sectors, and humanity. Without a doubt, aviation is one of the mostly affected industry. Those days will hopefully pass and will give way to a new rise of aviation, possibly with new tools and new applications. One of those tools is inevitably Artificial Intelligence (AI), and its applications to aviation.
The fact that AI has the potential to open doors for new possibilities in aviation has been known for a while, and a variety of organizations have already been issuing papers mentioning its potential and the expected challenges of its application [1,2,3,4].
The very nature of AI enables new functions, but also makes it difficult to be understood by us humans. The fact that it is not deterministic helps to cope with the inherently non-deterministic nature of the world, but makes it difficult to be interpreted by the humans, who are also inherently non-deterministic, yet search for determinism to predict the next outcome. First of all, it might be useful to give a tiny bit of insight of what I mean by determinism. Well, here, I am not necessarily talking about a nice sophisticated definition of determinism, but rather the understanding of an aerospace engineer who has touched AI from one corner at one point. A deterministic system is a system, that gives the same output given the same input. Let’s say if your system is the airplane, given the same control inputs to it, you would expect it to behave in the same manner – provided of course that all the other flight conditions remain equal. Until today, all the systems used on-board an aircraft were deterministic (well, we can discuss here if a pilot behaves deterministically or not…) and the standards and certification strategies applied to it are developed to accommodate them. This gives an ease for the certification bodies to be sure how the system will behave, so that the system can be designed to achieve the expected safety requirements. But yet, just at that moment, we also sacrifice the ability to handle the unexpected situations, since we have already designed the system to deal with the expected (or in an expected range) in an expected manner. Thus, dealing with unexpected situations, might be calling for solutions of an unexpected, unpredictable nature. Then, how can we utilize AI systems, which can learn, and tackle more complicated situations but still behave safely? Do we need a new understanding of safety, do we need to look for solutions that will give answers within a safe bound even if not necessarily deterministic? How do we deal with that?
Another point is, determinism is not the only thing missing for AI solutions to be able to be covered by current certification methods; Machine Learning (ML) methods cannot be certified with current tools even if they are deterministic. As an example, if we train a classifier that will distinguish faulty phases of a flight with respect to nominal flights, and then implement it, and not change the already trained model, then the model we have is deterministic. And yet, due to some other issues such as data dependency and explainability – which we will discuss in upcoming articles – today, we cannot certify a deterministic machine learning application.
With those and similar questions in mind, Europe and US are merging their power to offer the industries the chance to enjoy the advantages of AI in aviation (not all of them necessarily new, but newly efficient). In Europe, EUROCAE WG-114 Artificial Intelligence working group aims to develop technical standards and tools to guide development and certification efforts of aeronautical systems using AI technologies. The efforts of the working group initiated with the development of a statement of concern document. Being a part of EUROCAE, the Ecole Nationale de l’Aviation Civile (ENAC) also contributes to the efforts of WG-114. Meanwhile, a parallel effort in the US by SAE, G-34 Committee for Artificial Intelligence in Aviation, decides to join the efforts of EUROCAE WG-114, thanks to the common goals shared by the two organization. Although this merge did not yet officially take place, it will enable a harmonized and enriched standardization of AI for aviation.
The standardization effort of AI for aviation systems in the course of the working group encompasses both the onboard and ground systems. The first challenge selected is to focus on the standardization of offline Neural Networks (i.e. that will not modify themselves once the aircraft is in flight), thanks to its wide expected utilization. Although it is the first phase of the effort and statement of concerns document is not yet officially published (the document is almost ready), EASA’s guideline for AI shows that the marriage between AI and Aviation is not that far .
AI driven flight control software is a personal interest of mine, being a person of controls in aviation. The feedback mechanism is very powerful and yet dangerous if not used properly, so giving feedback to a system by an AI driven control seems to be not the first application of AI in aviation. But yet, EASA foresees the first usable guidance for a more autonomous machine AI/ML integration to be published in 2024 and finalized in 2028 . So hopefully one day…
“Our most beautiful days- we haven’t seen them yet.” Nazim Hikmet
 Roadmap for intelligent systems in aerospace, AIAA, Intelligent systems technical committee
 Artificial Intelligence with Applications for Aircraft, FAA
 Explainable Artificial Intelligence, XAI, DARPA
 Challenges in the Verification of Reinforcement Learning Algorithms – NASA
 Artificial Intelligence Roadmap – EASA 2020