Moralization is a social-psychological process through which morally neutral issues take on moral significance. Often linked to health and disease, moralization may sometimes lead to good outcomes; yet moralization is often detrimental to individuals and to society as a whole. It is therefore important to be able to identify when moralization is inappropriate. In this paper, we offer a systematic normative approach to the evaluation of moralization. We introduce and develop the concept of ‘mismoralization’, which is when moralization is metaethically (...) unjustified. In order to identify mismoralization, we argue that one must engage in metaethical analysis of moralization processes while paying close attention to the relevant facts. We briefly discuss one historical example and two contemporary cases related to COVID-19 that we contend to have been mismoralized in public health. We propose a remedy of de-moralization that begins by identifying mismoralization and that proceeds by neutralizing inapt moral content. De-moralization calls for epistemic and moral humility. It should lead us to pull away from our tendency to moralize—as individuals and as social groups—whenever and wherever moralization is unjustified. (shrink)

Robotization is an increasingly pervasive feature of our lives. Robots with high degrees of autonomy may cause harm, yet in sufciently complex systems neither the robots nor the human developers may be candidates for moral blame. John Danaher has recently argued that this may lead to a retribution gap, where the human desire for retribution faces a lack of appropriate subjects for retributive blame. The potential social and moral implications of a retribution gap are considerable. I argue that the retributive (...) intuitions that feed into retribution gaps are best understood as deontological intuitions. I apply a debunking argument for deontological intuitions in order to show that retributive intuitions cannot be used to justify retributive punishment in cases of robot harm without clear candidates for blame. The fundamental moral question thus becomes what we ought to do with these retributive intuitions, given that they do not justify retribution. I draw a parallel from recent work on implicit biases to make a case for taking moral responsibility for retributive intuitions. In the same way that we can exert some form of control over our unwanted implicit biases, we can and should do so for unjustifed retributive intuitions in cases of robot harm. (shrink)

In recruitment, promotion, admission, and other forms of wealth and power apportion, an evaluator typically ranks a set of candidates in terms of their perceived competence. If the evaluator is prejudiced, the resulting ranking will misrepresent the candidates’ actual ranking. This constitutes not only a moral and a practical problem, but also an epistemological one, which begs the question of what we should do – epistemologically – to mitigate it. The article is an attempt to begin to answer this question. (...) I first explore the presuppositions that must obtain for individual interventions to likely yield positive epistemological effects in ranking situations. I then compare these with the corresponding presuppositions of a novel, ‘post hoc’ approach to deprejudicing due to Jönsson and Sjödahl (Episteme 14(4):499–517, 2017), which does not attempt to change evaluators but attempts to increase the veracity of the rankings they produce after the fact (but before the rankings give rise to discriminatory effects) using statistical methods. With these two sets of presuppositions in place, I describe the limitations imposed by each presupposition on its intervention, compare presuppositions across the two kinds of interventions, and conclude that the two kinds of interventions importantly complement each other by having fairly disjoint, but non–conflicting, presuppositions. The post hoc intervention can thus complement an individual intervention (and vice versa) in situations where both are applicable (by adding further increases in veracity), but also by applying to situations where that intervention is not applicable (and thereby increase veracity in situations beyond the reach of that intervention). (shrink)