Designing AI for mental health diagnosis: responding to critics

Introduction

This commentary aims to respond to some criticism against our paper entitled ‘Designing AI for Mental Health Diagnosis: Challenges from sub-Saharan value-laden Judgments on Mental Health Disorders’.1 While we are sympathetic to the invaluable critiques of some authors, we show that some misunderstanding arises in reading our conceptualisation of the condition we use as a central example of disease in our paper.

A brief recap of our argument

We argue, in our paper, that there are obvious context-specific value judgments when it comes to mental health disorders, which obscures an application of generic machine learning (ML) in the diagnosis of these conditions. To contextualise our contention, our understanding of mental health disorder is grounded in Jerome Wakefield’s2 hybrid theorisation of disease as that which encompasses aspects of the normativist and naturalist accounts of health. Wakefield theorises that facts are necessary but insufficient to make a condition…

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