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
John Vincent 