Paul Taylor

The results of REF2021, the latest iteration of the Research Excellence Framework assessing the quality of research at UK universities, were published last week. My institution, UCL, is boasting that it came second, above Cambridge and beaten only by Oxford. Cambridge is boasting that it came third, but behind Imperial and the Institute of Cancer Research; institutions that shouldn’t quite count, it implies, since neither covers the full range of academic endeavour. Imperial, however, is clear that it has been shown to be the UK’s top university. The same claim is made by Oxford.

Researchers led by a team from Emory University recently announced that they had used artificial intelligence to predict patients’ self-reported racial identity from medical images. It is an unexpected, unsettling result.

As chest X-rays of Covid-19 patients began to be published in radiology journals, AI researchers put together an online database of the images and started experimenting with algorithms that could distinguish between them and other X-rays. Early results were astonishingly successful, but disappointment soon followed. The algorithms were responding not to signs of the disease, but to minor technical differences between the two sets of images, which were sourced from different hospitals: such things as the way the images were labelled, or how the patient was positioned in the scanner. It’s a common problem in AI. We often refer to ‘deep’ machine learning because we think of the calculations as being organised in layers and we now use many more layers than we used to, but what is learned is nevertheless superficial.