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

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. [...]