Machine learning for mental health diagnosis: Tackling contributory injustice and epistemic oppression

Giorgia Pozzi*, Michiel De Proost

*Corresponding author for this work

Research output: Contribution to journalComment/Letter to the editorScientificpeer-review

Abstract

In their contribution, Ugar and Malele shed light on an often overlooked but crucial aspect of the ethical development of machine learning (ML) systems to support the diagnosis of mental health disorders. The authors restrain their focus on pointing to the danger of misdiagnosing mental health pathologies that do not qualify as such within sub-Saharan African communities and argue for the need to include population-specific values in these technologies’ design. However, an analysis of the nature of the harm caused to said populations once their values remain unrecognised is not offered. [...]
Original languageEnglish
Pages (from-to)596-597
Number of pages2
JournalJournal of medical ethics
Volume50
Issue number9
DOIs
Publication statusPublished - 2024

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Ethics

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