Demographic diversity might often be present in a group without group members noticing it. What are the epistemic effects if they do? Several philosophers and social scientists have recently argued that when individuals detect demographic diversity in their group, this can result in epistemic benefits even if that diversity doesn’t involve cognitive differences. Here I critically discuss research advocating this proposal, introduce a distinction between two types of detection of demographic diversity, and apply this distinction to the theorizing on diversity (...) in science. Focusing on ‘invisible’ diversity, I argue that in one common kind of group in science, if group members have full insight into their group’s diversity, this is likely to create epistemic costs. These costs can be avoided and epistemic benefits gained if group members only partly detect their group’s diversity. There is thus an epistemic reason for context-dependent limitations on scientists’ insight into the diversity of their group. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...) some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (shrink)

It has recently been suggested that politically motivated cognition leads progressive individuals to form beliefs that underestimate real differences between social groups and to process information selectively to support these beliefs and an egalitarian outlook. I contend that this tendency, which I shall call ‘egalitarian confirmation bias’, is often ‘Mandevillian’ in nature. That is, while it is epistemically problematic in one’s own cognition, it often has effects that significantly improve other people’s truth tracking, especially that of stigmatized individuals in academia. (...) Due to its Mandevillian character, egalitarian confirmation bias isn’t only epistemically but also ethically beneficial, as it helps decrease social injustice. Moreover, since egalitarian confirmation bias has Mandevillian effects especially in academia, and since progressives are particularly likely to display the bias, there is an epistemic reason for maintaining the often-noted political majority of progressives in academia. That is, while many researchers hold that diversity in academia is epistemically beneficial because it helps reduce bias, I argue that precisely because political diversity would help reduce egalitarian confirmation bias, it would in fact in one important sense be epistemically costly. (shrink)

The present article critiques standard attempts to make philosophy appear relevant to the scientific study of well-being, drawing examples in particular from works that argue for fundamental differences between different forms of wellbeing, and claims concerning the supposedly inherent normativity of wellbeing research. Specifically, it is argued that philosophers in at least some relevant cases fail to apply what is often claimed to be among their core competences: conceptual rigor—not only in dealing with the psychological construct of flow, but also (...) in relation to apparently philosophical concepts such as normativity, objectivity, or eudaimonia. Furthermore, the uncritical use of so-called thought experiments in philosophy is shown to be inappropriate for the scientific study of wellbeing. As an alternative to such philosophy-as-usual, proper attention to other philosophical traditions is argued to be promising. In particular, the philosophy of ZhuangZi appears to concord well with today’s psychological knowledge, and to contain valuable ideas for the future development of positive psychology. (shrink)

Metaphilosophy, Volume 53, Issue 1, Page 100-133, January 2022.

Epistemic injustice occurs when we fail to appropriately respect others as epistemic agents. Philosophers building on the work of Miranda Fricker, who introduced the concept, have focused on epistemic injustices involving certain social categories, particularly race and gender. Can there be epistemic injustice attached to political conviction and affiliation? I argue yes: politics can be a salient social category that draws epistemic injustice. Epistemic injustices might also be intersectional, based on the overlap of politics and some other identity category like (...) race or sex. Further, and more provocatively, I argue that political minorities in academia, in particular conservatives and libertarians, are most likely the victims of epistemic injustice on the basis of their politics. Such epistemic injustices might even be routine. Although more limited in scope and severity than other forms of epistemic injustice, political epistemic injustices in academia ought to be of special concern from a standpoint of social justice because of the academy’s central role in knowledge production and dissemination. (shrink)