Who’s in control?

The subject of artificial intelligence (AI) in an aircraft cockpit stirs-up reactions that are both passionate and pragmatic. Maybe, it’s a Marmite issue[1]. Mention of the subject triggers an instant judgement. 

Large passenger transport civil aircraft are flown by two human operators. Decisions are made by those two human operators. They are trained and acquire experience doing the job of flying. A word that has its origins in the marine world is used to describe their role – pilot.

One of my roles, early on in my career, was to lead the integration of a cockpit display system into a large new helicopter[2]. New, at the time. The design team, I was part of comprised of people with two different professional backgrounds. One had an engineering background, like me, and the other had qualification associated with psychology. The recognition that an aircraft cockpit is where the human and machine meet is not new. A lot of work was done in simulation with flight crews. 

The first generation of jet aircraft put the pilot in full-time command. It’s as we moved from purely mechanical interactions with aircraft, the balance of flight control has been shared between pilot and aircraft systems. There’s no doubt, in the numbers, that this has improved aviation safety.

Nobody is calling for the removal of aircraft autopilot systems. Much of the role of the formerly required flight engineer has been integrated into the aircraft systems. Information is compressed and summarised on flat screen displays in the aircraft cockpit.

Today, AI is not just one thing. There’s a myriad of different types and configurations, some of which are frozen and some of which are constantly changing as they learn and grow. That said, a flawless machine is a myth. Now, that’s a brave statement. We are generations away from a world where sentient machines produce ever better machines. It’s the stuff of SiFi.

As we have tried to make ever more capable machines, failures are a normal part of evolution. Those cycles of attempts and failures will need to lead into the billions and billions before human capabilities are fully matched. Yes, I know that’s an assertion, but it has taken humans more than a million years to get to have this discussion. That’s with our incredible brains.

What AI can do well is to enhance human capabilities[3]. Let’s say, of all the billions of combinations and permutations, an aircraft in flight can experience, a failure that is not expected, not trained, and not easily understood occurs. This is where the benefits and speed of AI can add a lot. Aircraft system using AI should be able to consider a massive number of potential scenarios and provide a selection of viable options to a flight crew. In time critical events AI can help.

The road where AI replaces a pilot in the cockpit is a dead end. The road where AI helps a pilot in managing a flight is well worth pursuing. Don’t set the goal at replacing humans. Set the goal at maximising the unique qualities of human capabilities.


[1] https://www.macmillandictionary.com/dictionary/british/marmite_2

[2] https://en.wikipedia.org/wiki/AgustaWestland_AW101

[3] https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it

First Encounter

My first encounter with what could be classed as early Artificial Intelligence (AI) was a Dutch research project. It was around 2007. Let’s first note, a mathematical model isn’t pure AI, but it’s an example of a system that is trained on data.

It almost goes without saying that learning from accidents and incidents is a core part of the process to improve aviation safety. A key industry and regulatory goal is to understand what happened when things go wrong and to prevent a repetition of events.

Civil aviation is an extremely safe mode of transport. That said, because of the size of the global industry there are enough accidents and incidents worldwide to provide useful data on the historic safety record. Despite significant pre-COVID pandemic growth of civil aviation, the number of accidents is so low that further reduction in numbers is providing hard to win.

What if a system was developed that could look at all the historic aviation safety data and make a prediction as to what accidents might happen next?

The first challenge is the word “all” in that compiling such a comprehensive record of global aviation safety is a demanding task. It’s true that comprehensive databases do exist but even within these extremely valuable records there are errors, omissions, and summary information. 

There’s also the kick back that is often associated with record keeping. A system that demands detailed record keeping, of even the most minor incident can be burdensome. Yes, such record keeping has admirable objectives, but the “red tape” wrapped around its objectives can have negative effects.

Looking at past events has only one aim. That’s to now do things to prevent aviation accidents in the future. Once a significant comprehensive database exists then analysis can provide simple indicators that can provide clues as to what might happen next. Even basic mathematics can give us a trend line drawn through a set of key data points[1]. It’s effective but crude.

What if a prediction could take on-board all the global aviation safety data available, with the knowledge of how civil aviation works and mix it in such a way as to provide reliable predictions? This is prognostics. It’s a bit like the Delphi oracle[2]. The aviation “oracle” could be consulted about the state of affairs in respect of aviation safety. Dream? – maybe not.

The acronym CAT normally refers to large commercial air transport (CAT) aeroplanes. What this article is about is a Causal model for Air Transport Safety (CATS)[3]. This research project could be called an early use of “Big Data” in aviation safety work. However, as I understand it, the original aim was to make prognostics a reality.

Using Bayesian network-based causal models it was theorised that a map of aviation safety could be produced. Then it could be possible to predict the direction of travel for the future.

This type of quantification has a lot of merit. It has weaknesses, in that the Human Factor (HF) often defies prediction. However, as AI advances maybe causal modelling ought to be revised. New off-the-shelf tools could be used to look again at the craft of prediction.


[1] https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Air_safety_statistics_in_the_EU

[2] https://www.history.com/topics/ancient-greece/delphi

[3] https://open.overheid.nl/documenten/ronl-archief-d5cd2dc7-c53f-4105-83c8-c1785dcb98c0/pdf