MD
Michiel De Proost
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Considering the overall shortage of therapists to meet the psychological needs of vulnerable populations, AI-based technologies are often seen as a possible remedy. Particularly smartphone apps or chatbots are increasingly used to offer mental health support, mostly through cognitive behavioral therapy. The assumption underlying the deployment of these systems is their ability to make mental health support accessible to generally underserved populations. Hence, this seems to be aligned with the fundamental biomedical principle of justice understood in its distributive meaning. However, considerations of the principle of justice in its epistemic significance are still in their infancy in the debates revolving around the ethical issues connected to the use of mental health chatbots. This paper aims to fill this research gap, focusing on a less familiar kind of harm that these systems can cause, namely the harm to users in their capacities as knowing subjects. More specifically, we frame our discussion in terms of one form of epistemic injustice that such practices are especially prone to bring about, i.e., participatory injustice. To make our theoretical analysis more graspable and to show its urgency, we discuss the case of a mental health Chatbot, Karim, deployed to deliver mental health support to Syrian refugees. This case substantiates our theoretical considerations and the epistemo-ethical concerns arising from the use of mental health applications among vulnerable populations. Finally, we argue that conceptualizing epistemic participation as a capability within the framework of Capability Sensitive Design can be a first step toward ameliorating the participatory injustice discussed in this paper.
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Considering the overall shortage of therapists to meet the psychological needs of vulnerable populations, AI-based technologies are often seen as a possible remedy. Particularly smartphone apps or chatbots are increasingly used to offer mental health support, mostly through cognitive behavioral therapy. The assumption underlying the deployment of these systems is their ability to make mental health support accessible to generally underserved populations. Hence, this seems to be aligned with the fundamental biomedical principle of justice understood in its distributive meaning. However, considerations of the principle of justice in its epistemic significance are still in their infancy in the debates revolving around the ethical issues connected to the use of mental health chatbots. This paper aims to fill this research gap, focusing on a less familiar kind of harm that these systems can cause, namely the harm to users in their capacities as knowing subjects. More specifically, we frame our discussion in terms of one form of epistemic injustice that such practices are especially prone to bring about, i.e., participatory injustice. To make our theoretical analysis more graspable and to show its urgency, we discuss the case of a mental health Chatbot, Karim, deployed to deliver mental health support to Syrian refugees. This case substantiates our theoretical considerations and the epistemo-ethical concerns arising from the use of mental health applications among vulnerable populations. Finally, we argue that conceptualizing epistemic participation as a capability within the framework of Capability Sensitive Design can be a first step toward ameliorating the participatory injustice discussed in this paper.
The principle of trust has been placed at the centre as an attitude for engaging with clinical machine learning systems. However, the notions of trust and distrust remain fiercely debated in the philosophical and ethical literature. In this article, we proceed on a structural level ex negativo as we aim to analyse the concept of “institutional distrustworthiness” to achieve a proper diagnosis of how we should not engage with medical machine learning. First, we begin with several examples that hint at the emergence of a climate of distrust in the context of medical machine learning. Second, we introduce the concept of institutional trustworthiness based on an expansion of Hawley’s commitment account. Third, we argue that institutional opacity can undermine the trustworthiness of medical institutions and can lead to new forms of testimonial injustices. Finally, we focus on possible building blocks for repairing institutional distrustworthiness.
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The principle of trust has been placed at the centre as an attitude for engaging with clinical machine learning systems. However, the notions of trust and distrust remain fiercely debated in the philosophical and ethical literature. In this article, we proceed on a structural level ex negativo as we aim to analyse the concept of “institutional distrustworthiness” to achieve a proper diagnosis of how we should not engage with medical machine learning. First, we begin with several examples that hint at the emergence of a climate of distrust in the context of medical machine learning. Second, we introduce the concept of institutional trustworthiness based on an expansion of Hawley’s commitment account. Third, we argue that institutional opacity can undermine the trustworthiness of medical institutions and can lead to new forms of testimonial injustices. Finally, we focus on possible building blocks for repairing institutional distrustworthiness.
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. [...]
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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. [...]