Algorithmic autoimmunity in the NHS: Radicalisation and the clinic

Abstract This article explores the extension of counter-radicalisation practice into the National Health Service (NHS). In the 2011 reformulation of the UK Prevent strategy, the NHS became a key sector for the identification and suppression of ‘radicalisation’. Optometrists, dentists, doctors and nurses have been incorporated into counter-terrorism and trained to report signs of radicalisation in patients and staff. This article explores how calculative modalities associated with big data and digital analytics have been translated into the non-digital realm. The surveillance of the whole of the population through the NHS indicates a dramatic policy shift away from linear profiling of those ‘suspect communities’ previously considered vulnerable to radicalisation. Fixed indicators of radicalisation and risk profiles no longer reduce the sample size for surveillance by distinguishing between risky and non-risky bodies. Instead, the UK government chose the NHS as a pre-eminent site for counter-terrorism because of the large amount of contact it has with the public. The UK government is developing a novel counter-terrorism policy in the NHS around large-N surveillance and inductive calculation, which demonstrates a translation of algorithmic modalities and calculative regimes. This article argues that this translation produces an autoimmune moment in British security discourse whereby the distinction between suspicious and non-suspicious bodies has collapsed. It explores the training provided to NHS staff, arguing that fixed profiles no longer guide surveillance: rather, surveillance inductively produces the terrorist profile.