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Designing AI for mental health diagnosis: responding to critics
  1. Edmund Terem Ugar1,
  2. Ntsumi Malele2
  1. 1Department of Philosophy and Centre for Africa-China Studies, University of Johannesburg, Auckland Park, South Africa
  2. 2Department of Philosophy, University of Johannesburg, Auckland Park, South Africa
  1. Correspondence to Dr Edmund Terem Ugar, University of Johannesburg, Auckland Park, South Africa; teremedmund{at}gmail.com

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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 a disease. Wakefield claims that a disorder requires harm that involves values. Thus, values and facts must go concomitantly for a condition to be considered a disorder. Here, a disorder is conceived as something that is a ‘harmful dysfunction’. On the one hand, harmful is value laden—characterised by how a society conceives the notion due to their set norms. On the contrary, dysfunction is a solely scientific term due to a failure to function in accordance with the reference class of an organism. …

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Footnotes

  • Contributors n/a.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; internally peer-reviewed.

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