The significance of critical phenomenology for psychiatric praxis has yet to be expounded. In this paper, I argue that the adoption of a critical phenomenological stance can remedy localised instances of hermeneutical injustice, which may arise in the encounter between clinicians and patients with psychosis. In this context, what is communicated is often deemed to lack meaning or to be difficult to understand. While a degree of un-shareability is inherent to subjective life, I argue that issues of unintelligibility can be (...) addressed by shifting from individualistic conceptions of understanding to an interactionist view. This takes into account the contextual, historical and relational background within which meaning is co-constituted. I conclude by providing a corrective for hermeneutical injustice, which entails a specific attentiveness towards the person’s subjectivity, a careful sensitivity to contingent meaning-generating structures, and a degree of hermeneutical flexibility as an attitude of openness towards alternative horizons of possibility. (shrink)

Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby consider how ML systems may silence patients’ voices and relativise (...) the credibility of their opinions, which undermines their overall credibility status without valid moral and epistemic justification. More specifically, I argue that withholding credibilitydue tohow ML systems operate can be particularly harmful to patients and, apart from adverse outcomes, qualifies as a form of testimonial injustice. I make my case for testimonial injustice in medical ML by considering ML systems currently used in the USA to predict patients’ risk of misusing opioids (automated Prediction Drug Monitoring Programmes, PDMPs for short). I argue that the locus of testimonial injustice in ML-mediated medical encounters is found in the fact that these systems are treated asmarkers of trustworthinesson which patients’ credibility is assessed. I further show how ML-based PDMPs exacerbate and further propagate social inequalities at the expense of vulnerable social groups. (shrink)