Better Living Through Algorithms

Abstract

The use of machine learning and algorithmic processes to screen for mental illnesses, and to propose potential treatments, has caused some to be concerned about the possibility of increasingly impersonal and invasive forms of psychiatric surveillance. For some, the rise of big data and algorithmic psychiatry presents the possibility of a future where the mentally ill are increasingly dominated by machines. Concerns about the use of algorithms in the diagnosis of mental illnesses, and the devising of treatments, perhaps overlooks the extent to which an algorithmic revolution has been facilitated by existing human-centred psychological therapies. Through the dominance of cognitive-behavioural therapy (CBT), commonly experienced mental illnesses such as anxiety or depression have come to be understood as the result of faulty processes of recursive thinking. The model of consciousness that underpins cognitive-behavioural approaches, suggests that to be human is to already engage in forms of cognition that are open to algorithmic manipulation, insofar as to think is to produce recursive rules of self-conduct. Accordingly, this paper seeks to articulate the inhuman model of thought that is assumed by CBT, and to consider how it has opened the space for an algorithmic revolution in mental health.