1 month, 3 days ago

Near Miss

Link: http://feedproxy.google.com/~r/Soapbox/~3/J4r4429oRCM/near-miss.html

A serious aviation incident in the news today. A plane took off from Birmingham last year with insufficient fuel, because the weight of the passengers was incorrectly estimated. This is being described as an IT error.

As Cathy O’Neil’s maxim reminds us, algorithms are opinions embedded in code. The opinion in this case was the assumption that the prefix Miss referred to a female child. According to the official report, published this week, this is how the prefix is used in the country where the system was programmed

In this particular flight, 38 adult women were classified as Miss, so the algorithm estimated their weight as 35 kg instead of 69 kg.

The calculation error was apparently compounded by several human factors.

  • A smaller discrepancy had been spotted and corrected on a previous flight. 
  • The pilot noticed that there seemed to be an unusually high number of children on the flight, but took no action because the pandemic had disrupted normal expectations of passenger numbers.
  • The software was being upgraded, but the status of the fix at the time of the flight was unclear. There were other system-wide changes being implemented at the same time, which may have complicated the fix.
  • Guidance to ground staff to double-check the classification of female passengers was not properly communicated and followed, possibly due to weekend shift patterns.

As Dan Nguyen points out, there have been previous incidents resulting from incorrect assumptions about passenger weight. But I think we need to distinguish between factual errors (what is the average weight of an adult passenger) and classification errors (what exactly does the Miss prefix signify).

There is an important lesson for data management here. You may have a business glossary or data dictionary that defines an attribute called Prefix and provides a list of permitted values. But if different people (different parts of your organization, different external parties) understand and use these values to mean different things, there is still scope for semantic confusion unless you make the meanings explicit.

AAIB Bulletin 4/2021 (April 2021) https://www.gov.uk/government/publications/air-accident-monthly-bulletin-april-2021

Tui plane in ‘serious incident’ after every ‘Miss’ on board was assigned child’s weight (Guardian, 9 April 2021)

For further discussion and related examples, see Dan Nguyen’s Twitter thread https://twitter.com/dancow/status/1380188625401434115