Reproductive genetic carrier screening is increasingly being offered more widely, including to people with no family history or otherwise elevated chance of having a baby with a genetic condition. There are valid reasons to reject a prevention-focused public health ethics approach to such screening programs. Rejecting the prevention paradigm in this context has led to an emphasis on more individually-focused values of freedom of choice and fostering reproductive autonomy in RCS. We argue, however, that population-wide RCS has sufficient features in (...) common with other public health screening programs that it becomes important also to attend to its public health implications. Not doing so constitutes a failure to address the social conditions that significantly affect people’s capacity to exercise their reproductive autonomy. We discuss how a public health ethics approach to RCS is broader in focus than prevention. We also show that additional values inherent to ethical public health—such as equity and solidarity—are essential to underpin and inform the aims and implementation of reproductive carrier screening programs. (shrink)

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)