A false equivalence?

Pozzi objects to the introduction of machine-learning risk prediction tools in clinical care, due to their being ‘epistemically opaque’, often inaccurate, and a threat to patients’ testimonial justice. Through the example of psychiatry, I suggest this stance idealises traditional models of clinical practice. I propose that clinicians’ judgements are often equally vulnerable to opaque subjectivity, bias and epistemic injustice. For instance, the use of clinical observation or collateral interviewing might be conceptualised as a threat to testimonial justice, by neglecting patients’ own perspectives or voices. In practice, clinical risk prediction often follows inconsistent and somewhat subjective clinical heuristics, which are difficult to summarise or evaluate. Further, although Pozzi diminishes machine-learning …