In this paper, we consider how latent variables of mainstream quantitative psychology fits with two different models of scientific kinds. On the one hand, there is a good reason to think they fit with taxonomic and predictive success criteria that are popular within an epistemic understanding of scientific kinds. On the other hand, they conflict with widely shared person-based ontological commitments that underwrite psychological kinds because this research rests in large part on between-individual studies. We explore the implications of the (...) different views on scientific kinds and argue that this is indeed a dilemma for the scientific discipline of psychology. (shrink)

The goal of the debunking social constructionist is to reveal as social kinds that are widely held to be natural (or, in some cases, to reveal as more deeply social kinds that are already widely recognized to be social). The prominent approach to such debunking has been to make a case for thinking that the individuation conditions for membership in the kinds in question are in fact social (or in fact more deeply social than has previously been recognized). In this (...) paper, I argue that adopting the prominent approach to debunking prevents one from answering the implicit question being posed by the debunker, namely, the question of how a plausibly socially constituted kind can come to appear more natural than it in fact is. I then sketch an alternative way of understanding social kinds that enables us to answer the appearance question while remaining neutral on the nature of social kinds. In particular, I argue that the mechanism of covert normativity explains causally the persistence of the social kinds of interest to the debunker, and so also provides an answer to the appearance question. (shrink)

We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.