Wired reports that the government’s ‘Nudge Unit’ is experimenting with using machine learning algorithms to rate how well schools and doctors’ surgeries are performing.
For the last year, The Behavioural Insights Team (BIT) has been trialling machine learning models that can crunch through publicly available data to help automate some of the decisions made by bodies such as Ofsted, which inspects schools, and the Care Quality Commission, which regulates health and social care in England. Michael Sanders, head of research at the BIT says it is working with Ofsted to put the technology into use during 2018. “We’re working with them to feed into variations on our model and to improve it using additional data that they have that isn’t public,” he says.
The school-evaluating algorithm pulls together data from a large number of sources to decide whether a school is potentially performing inadequately. It is said the system can help to identify more schools that are inadequate, when compared to random inspections. Information taken into account includes how many children are on free school meals, how much teachers are paid and the number of teachers for each subject. This data is then paired with data scraped from reviews of schools submitted by parents on the Ofsted-run website Parent View.
Data on student’s ethnicity and religion were deliberately excluded from the dataset in an effort to prevent algorithmic bias. Although some factors will influence the algorithm’s decision more than others, Sanders refused to say what those factors where. This is partly because he doesn’t want schools to know how the algorithm makes its decisions, and partly because it is difficult to know exactly how these algorithms are working, he says. “The process is a little bit of a black box – that’s sort of the point of it,” he says.
The decision how to use this technology is ultimately up to Ofsted and the other organisations working with the BIT. But Sanders predicts it could be used to reduce the number of school inspections by a third, freeing up Ofsted employees to work more closely with other schools to improve them. “You can focus these interventions more carefully on the people who most need it and are most likely to benefit from it,” he says.
According to the BIT, machine learning models can identify six times as many inadequate schools than random inspections alone. It claims similar machine learning models it has produced can also identify 95 per cent of inadequate GP surgeries. The BIT has also trialled versions of the model that help social workers prioritise assessments and predict where serious traffic collisions will occur. It has not published any data on these internal trials.
Read more about the BIT trials UK’s Nudge Unit tests machine learning to rate schools and GPs
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