Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous (...) treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine. (shrink)

The movement of a pendulum is often used as a metaphor to represent the history of twentieth century American psychiatry. On this view, American psychiatry evolved by swinging back and forth between two schools of thought in constant competition: somatic accounts of mental illness and psychodynamic ones. I argue that this narrative partly misrepresents the actual development of American psychiatry. I suggest that there were some important exchanges of ideas and practices in the transition from German biological approaches to American (...) psychodynamic approaches. In particular, two kinds of pragmatism played an important role in this transition: Kraepelin’s methodological pragmatism, and pragmatic values present in the American psychiatric context, due in part to the influence of William James. From a historical standpoint, I suggest that the metaphor of the pendulum doesn’t capture the full complexities of this shift in psychiatry at the turn of the century; from a philosophical standpoint, my discussion brings to light two strands of pragmatism salient to scientific psychiatry. (shrink)

The article advances a new way of thinking about classifications in general and the classification of mental disorders in particular. By applying insights from measurement practice to the context of classification, I defend a notion of epistemic accuracy that allows one to evaluate and improve classifications by comparing different classifying methods to each other. Progress in classification arises from the mutual development of classification systems and classifying methods. Based on this notion of accuracy, the article illustrates with an example how (...) psychiatric classifications can be improved via circumscribed comparisons of different perspectives on mental disorders, without relying on complete models of their complex aetiology. When applying this strategy, the traditional opposition between symptom-based and causal approaches is of little consequence for making progress in the epistemic accuracy of psychiatric classification. (shrink)