Algorithm to suggest food inspections abandoned
Published:
UK food inspectors have abandoned plans to use an algorithm in part of their food hygiene rating system after a £100,000 trial.
Fifteen local authorities took part in the six-week pilot in 2022. But in the end many local authorities found their own solutions to a post-Covid food inspection backlog, and the Food Standards Agency (FSA) said the automation raised too many ethical questions.
A “da” rating of 4 on an eatery window in Wales. Food hygiene here is “good”
The technology predicted two things: the hygiene rating of a food outlet that was waiting for inspection, and the probability that it would be broadly clean (a 3, 4 or 5 rating).
Screenshot of (part of) how the tool might respond when asked about a specific local authority, with the names of the area’s food outlets removed
Faced with a simple but pressing question after Covid, “Who should we inspect first?” the FSA paid the company Cognizant to build this technology. The model was to be used to identify which cases were most urgent, leaving local authority inspectors to establish the actual rating after a visit.
The code used to train the computer model was released in response to a Freedom of Information request along with an outline of the kinds of data that, once supplied to the computer system, would generate the two predictions.
A more detailed version of the machine’s response shows not just if an outlet is predicted to get a compliant rating (3-4-5) or non-compliant rating (0-1-2) but how likely it is to fall into a compliant category.
Part of a response, showing how likely it is that food outlets will get a higher, ‘compliant’ score
This technology was first described on a government platform to list algorithms that are used in the public sector: the Algorithmic Transparency Recording Standard, which was launched as a trial in late 2021 by two government offices that work on data.
The details of just six algorithms were published the following year and publication resumed early this year with the details of an algorithm developed for the Cabinet Office.
The FSA technology was ultimately shelved because the Agency decided there were “ethical concerns” that data on demographics and the type of food prepared in different outlets “could result in biases for certain types of businesses”. Also, many councils had found their own solutions to the backlog.
How it works
Some of the code that trained the model from existing data and generated predictions for new data
There are two main aspects to how this worked: the data that was provided in order for the machine to infer the likelihood that a food outlet wasn’t clean, and the code that read the data, generated the inferences, and returned a recommendation.
Although we haven’t analysed the code in detail, we know already that it was based on three particular machine learning libraries: tensorflow, sklearn and lightgbm. (Libraries are third-party computer programs imported into some code to carry out particular tasks).
The data that the machine read, in order to reach its conclusions was of three kinds:
the Food Standards Agency’s own data: Is it a pub, or a hospital? Is it a chain restaurant? Which local authority is it in?
census information from the local authority area: population size, age structure and density; residents’ country of birth, passport and language; house prices and economic situation; employment data; life expectancy and disability rate.
retail information for app developers from an online database. As well as the kind of food business and the opening hours, this appears to have told the model if the food outlet could be described as Chinese, Asian, Middle Eastern or Indian.
What we don’t have is the training data, i.e. the above information tied to existing Food Safety scores. Only by reading historical cases which link outlets to scores would the machine have been able to infer the likelihood of a new outlet being clean or dirty.
Methodology
The government platform was brought to my attention by a talk at Open Data Camp 2023 in Wolverhampton; the Transparency Recording Standard makes available an outline of the technology.
After an FOI request, I was given an outline of the variables, along with the code used to build the computer model. A second FOI request returned two pieces of documentation: a user manual and an Ethical AI framework.