This thesis is an investigation into the nature of epistemic justification. It brings together themes from traditional, individual-centred epistemology, and collective, group-centred epistemology. The first half of the thesis is concerned with the question of whether rationality is epistemically permissive; that is, whether one body of evidence can rationalise more than one doxastic attitude. In chapter 1, I argue that permissive cases are best understood as epistemic standard conflicts. Doing so provides us with a novel understanding of the arbitrariness objection (...) against permissivism and enables us to reduce questions about epistemic permissibility to questions about the nature of incommensurability. In chapter 2, I show that the defended understanding of permissive cases generalises by defending it against an objection from self-fulfilling beliefs. In chapter 3, I demonstrate that we can use this view of epistemic rationality to generate so-called divergence arguments which show that the epistemic status of group-level attitudes and member-level attitudes can rationally diverge. In the second half of the thesis, I develop a novel evidentialist theory of epistemic justification, called Continuous Evidentialism. Continuous Evidentialism is inspired by some general methodological reflections (chapter 4), which suggest that we should opt for a theory of epistemic justification that analyses the epistemic status of group-level attitudes and member-level attitudes continuously. According to Continuous Evidentialism, to have a justified belief is to possess sufficient evidence and utilise that evidence in an epistemically responsible way when forming the belief. While I argue that we can reduce epistemic responsibility to higher-order evidentialist requirements. In chapter 5, I develop a theory of evidence, evidence possession and epistemic basing. Chapters 6 - 8, discuss various complications of the proposed theory, having to do with the alleged defeasibility of justification (chapter 6), epistemic responsibility (chapter 7), and the proposed reduction of epistemic responsibility (chapter 8). In chapter 9, I compare Continuous Evidentialism to various extant accounts. (shrink)

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.

Mark Schroeder’s Reasons First is admirable in its scope and execution, deftly demonstrating the theoretical promise of extending the reasons-first approach from ethics to epistemology. In what follows we explore how (not) to account for the evidence-that relation within the reasons-first program, we explain how factive content views of evidence can be resilient in the face of Schroeder’s criticisms, and we explain how knowledge from falsehood threatens Schroeder’s view of knowledge. Along the way we sketch a reliabilist account of the (...) reasons-for relation (inspired by Alston) and show how it limits the applicability of Schroeder’s arguments. (shrink)