The art, science and AI of reviewing global airline safety performance

If you want to review current airline safety performance you can ask your favourite AI function to do it for you. Microsoft’s appropriately named Copilot, perhaps?

And if you prefer to see the accident and incident detail item by item, and then crunch the numbers yourself, you can visit the Flight Safety Foundation’s excellent Aviation Safety Network site.

Faced with all this competition, it is easy to see why aviation publications these days are less likely to pay experts like me to write a review for them. For example, for more than the last four decades Flight International/FlightGlobal has commissioned me to review airline safety annually, with briefer reviews at each half-year point. For the last two years the publishers have dropped the January-June reviews, and have decided this year not to commission me to carry out the annual review of 2026.

I am not privy to decisionmaking processes within Flight International/FlightGlobal, so I genuinely don’t know whether they intend, in future, to generate an annual airline safety review internally, or to drop the accustomed review on the grounds that AI-generated reports and expert sites like the Aviation Safety Network are so easy for readers to access free of charge that it’s not worth allocating the resources to produce it.

Beyond those resouces there are subscription services like Cirium aviation analytics. Today’s professional media are constantly having to review what they cover and how they cover it in the light of AI, data suppliers, and social media developments.

Curious to find out what AI might make of my request to generate a summary review of global airline safety for the first six months of 2026 “in the style of David Learmount”, Chat GPT provided this:

The first six months of 2026 have reinforced a familiar truth about commercial aviation safety: the industry’s underlying safety performance remains exceptionally strong, but public confidence continues to be shaped by a handful of highly visible accidents and serious incidents. While individual events have prompted intense scrutiny, there is no compelling evidence that the global system has suffered a deterioration in safety standards. Rather, they have highlighted the enduring importance of disciplined operational execution, effective oversight, and continuous learning.The industry’s long-term trajectory remains positive, with accident rates still measured in fractions of a million flights and safety improvements driven by better technology, training, and data-led risk management. Yet the first half of the year has also served as a reminder that commercial aviation can never regard safety as an achievement that is complete. Every serious occurrence is an opportunity to identify latent weaknesses before they align, reaffirming that vigilance—not complacency—is the defining characteristic of a mature safety culture.

While not disagreeing with anything in that summary, I can honestly say that none of the actual sentences would ever be the product of my own pen! Most of those expert journalists I know who have carried out a similar exercise on a subject they know well report that the result is – inevitably – an assembly of terminally bland copy full of generalisations, even if the generalisations are accurate as far as they go.

So, is this the end of the journalistic road for human-generated expert reviews? Time will tell, but I will still go to the motoring journals for a personal review of cars before I consider buying a new one, partly because I want to tap into the writer’s years of experience, but mostly because I want to enjoy the process of becoming informed.

What do you think?

AI suggests why the Philadelphia medevac crash happened

Can artificial intelligence (AI) provide the factors behind aviation accidents? Maybe we should find out, because people can suddenly believe they are experts as a result of using it.

A reader contacted me on February 1st with the answer that an AI app provided when he asked it what caused the 31 January fatal Learjet air ambulance crash in Philadelphia.

Before discussing the AI’s verdict, here is what we actually know about the short flight: the Learjet 55 took off in the evening dusk (18:06:10 local time) from Philadelphia Northeast Airport runway 24, bound for Springfield, Missouri, with a stretchered patient and five other people on board. The temperature and dew point were both 9deg, the cloud-base was at 400-500ft, light wind and reasonable visibility.

When airborne the aircraft was told to turn right onto heading 290deg, and the pilot received an instruction from Tower to change frequency to Philadelphia Departure Control. He read the new frequency back correctly, and bid the Tower controller a good day.

According to ADS-B data, the Learjet had climbed rapidly to a maximum 1,650ft by about a minute after take-off (18:06:56) , then the aircraft entered a steep, uninterrupted descent to impact with the ground. The impact point was in a suburban area about 2.5nm from the airport, close to the runway extended centreline. The pilot never did make contact with Departure Control, and broadcast messages addressed to the flight by Departure did not receive a response.

According to initial reports by Philadelphia police, no-one on board survived the Learjet crash, one person on the ground was killed and 19 were injured, That is my summary of the basic known facts of what happened.

Meanwhile my reader who asked AI to provide him with an explanation for the crash told me he had, in his question, given the AI app (which he didn’t name) all the facts known at that point.

The executive summary part of the AI answer said this: “Preliminary data from ADS-B tracking, witness reports, and aviation system analysis suggest that Learjet 55 XA-UCI suffered a catastrophic runaway trim event (nose-down), leading to an unrecoverable dive and high-speed impact.” It also supplied what I would describe as cogent arguments to back this verdict up, but no actual evidence for the alleged runaway trim or the electrical fault that it proposed was the reason for it. The whole proposal, however, was delivered in a decidedly confident style.

I decided to take a different approach to test AI on the same subject. Given what we know happened, I asked Chat GPT whether the pilot suffering spatial disorientation as a result of somatogravic illusion could be the explanation for the Learjet accident? ChatGPT’s response first explained what somatogravic illusion is, then responded that, yes, it could indeed be a plausible explanation, but advised me to wait for the National Transportation Safety Board’s report.

Somatogravic illusion is an illusion generated by the delicate human inner ear balance organs when they are subjected to acceleration, linear or rotational. For example, passengers seated in the cabin of an aircraft beginning its acceleration along the runway for take-off can feel that the whole aircraft has tilted slightly nose-up, especially if they are looking straight ahead. But a glance out of the cabin window during the take-off run will prove that no such upward tilt has taken place.

Pilots experience the same somatogravic effect during take-off that passengers do, but since they are looking ahead out the cockpit windscreen – and providing the external visibility is good – their powerful visual sense will overcome the misleading feedback from their balance sensors.

If, however, the acceleration continues after take-off and the crew lose sight of the outside world because of darkness or entering cloud, the misleading feedback from their balance sensors returns. And the natural reaction to believing the aircraft’s nose is higher than it should be is to push forward on the control column, pushing the nose down. The physical feeling that a nose-down push is demanded can entirely overcome the intellectual information presented by the pilots’ flight instruments, because the latter is artificial, unlike powerful instinctive feelings or sight of a natural external horizon.

The Learjet series has a reputation for sporty performance. Its take-off acceleration and rate of climb when airborne are impressive. And the point in this short flight where it all appears to have gone wrong happens to occur at the moment when the pilot is likely to have taken his eyes off the flight instruments for a moment to change the radio frequency. The latter may be just coincidence, however.

There is no data here that could be regarded as evidence about the reasons for the Philadelphia crash, but I do know that the runaway trim explanation is plausible, and so is the pilot spacial disorientation theory.

There could be other reasons, however, and I know well after 45 years in this business that listing “what if” explanations is a waste of time because there are too many. The truth will out, via actual evidence. These days it does not take long, because investigators now strive to provide periodic interim factual reports which signpost the emerging truth.

But full understanding – and thus the ability confidently to act to prevent repetition – only comes with the full facts.