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Accepted Paper:
'So good it almost seems human’: designing empathy for digital therapy interventions
Jennifer Cearns
(University College London)
This paper considers how 'empathy’ is defined and designed into the code underpinning digital mental health interventions, often in a culturally normative and gendered manner, to offer reflections on how such technological developments may challenge our own conceptions of selfhood and subjectivity.
Paper long abstract:
Alongside a sharp increase in remote and virtual healthcare provision during the COVID-19 pandemic, there has also been a proliferation of apps and online services offering digital diagnostics, mood-tracking, and even AI-mediated therapy and ‘friendship’. Many digital healthcare companies have shifted their focus towards developing AI-assisted software which purports to assume the role of a talking therapist. Rather than speaking face-to-face with a therapist, some patients are encouraged to chat with a chatbot therapist (visualised through an animated avatar), the likes of which they can also co-design by selecting their preferred gender, hairstyle, or even ethnicity. This chatbot in turn draws on algorithms and AI to train itself as it ‘gets to know’ the patient. Such developments are increasing in the wake of a global pandemic and increasing pressures on NHS budgets for mental healthcare provision in the U.K.
This paper will consider how non-medical staff define and attempt to design ‘empathy’ into the code of these softwares and algorithms, often in a culturally normative and gendered manner. It will offer some reflections on what the development of technologies such as this may mean for our own conceptions of selfhood and subjectivity, especially in the light of algorithmic tools which ‘learn’ from the patient how best to respond to them, in a striking difference to the assumed position of neutrality that talking therapists are typically trained to take.