The article defends ubuntu against the assault by Enslin and Horsthemke (Comp Educ 40(4):545–558, 2004 ). It challenges claims that the Africanist/Afrocentrist project, in which the philosophy of ubuntu is central, faces numerous problems, involves substantial political, moral, epistemological and educational errors, and should therefore not be the basis for education for democratic citizenship in the South African context. The article finds coincidence between some of the values implicit in ubuntu and some of the values that are enshrined in the (...) constitution of South Africa and that on that basis argues that ubuntu has the potential to serve as a moral theory and a public policy. The educational upshot of this article’s argument is that South Africa’s educational policy framework not only places a high premium on ubuntu, which it conceives as human dignity, but it also requires the schooling system to promote ubuntu-oriented attributes and dispositions among the learners. The article finds similarities between ubuntu and bildung, whose key advocates, among others was German scholar and intellectual Wilhelm von Humboldt. It argues that it would be ethnocentric, and indeed silly to suggest that the ubuntu ethic of caring and sharing is uniquely African when some of the values which it seeks to promote can also be traced in various Eurasian philosophies. (shrink)

The debate between externalists and internalists in epistemology can be viewed as a disagreement about whether there are epistemic rights without corresponding duties or obligations. Taking an epistemic right to believe P as an authorization to not only accept P as true but to use P as a positive reason for accepting other propositions, the debate is about whether there are unjustified justifiers. It is about whether there are propositions that provide for others what nothing need provide for them—viz., reasons (...) for thinking them true. (shrink)

Machine learning (ML) systems play an increasingly relevant role in medicine and healthcare. As their applications move ever closer to patient care and cure in clinical settings, ethical concerns about the responsibility of their use come to the fore. I analyse an aspect of responsible ML use that bears not only an ethical but also a significant epistemic dimension. I focus on ML systems’ role in mediating patient–physician relations. I thereby consider how ML systems may silence patients’ voices and relativise (...) the credibility of their opinions, which undermines their overall credibility status without valid moral and epistemic justification. More specifically, I argue that withholding credibilitydue tohow ML systems operate can be particularly harmful to patients and, apart from adverse outcomes, qualifies as a form of testimonial injustice. I make my case for testimonial injustice in medical ML by considering ML systems currently used in the USA to predict patients’ risk of misusing opioids (automated Prediction Drug Monitoring Programmes, PDMPs for short). I argue that the locus of testimonial injustice in ML-mediated medical encounters is found in the fact that these systems are treated asmarkers of trustworthinesson which patients’ credibility is assessed. I further show how ML-based PDMPs exacerbate and further propagate social inequalities at the expense of vulnerable social groups. (shrink)