Abstract

Ugar and Malele write that generic machine learning (ML) technologies for mental health diagnosis would be challenging to implement in sub-Saharan Africa due to cultural specificities in how those conditions are diagnosed. For example, they say that in South Africa, the appearance of ‘schizophrenia’ might be understood as a type of spiritual possession, rather than a mental disorder caused by a brain dysfunction. Hence, a generic ML system is likely to ‘misdiagnose’ persons whose symptomatology matches that of schizophrenia in the South African context. The authors thus claim that ‘a generic or universal design cannot be effective given the heterogeneity of value judgements in defining what mental health disorders are in different contexts’.1 Should we take this to mean that ML systems ‘should not be designed with a generalised perception of mental disorders,’1 as the authors suggest? On the contrary, my view is that generic ML can be useful, with the caveat that issues of cultural sensitivity may engender translational challenges in various national and cultural contexts. Yet, if it can be demonstrated that a generic ML system can reliably and accurately pick up on shared symptomatologies across cultures despite what any individual or community might believe about their actual causes, this aspect is surely the function and value of diagnostic ML which we should take to matter. Cultural gaps in aetiological understandings of mental health conditions make it all the more important to advance generic ML as a calibrating diagnostic tool. In this commentary, therefore, I will endeavour to make a case for generic ML in mental healthcare. ### Aetiological (mis)understanding across cultures The fundamental complexity to which Ugar and Malele direct our attention is the fact that mental health conditions are attributed to variable causal explanations, rather than a …