Philosophical Issues, Volume 31, Issue 1, Page 355-373, October 2021.

One way of being responsible for an action is being praiseworthy for it. But what is the “praise” of which the praiseworthy agent is worthy? This paper provides a survey of answers to this question, i.e. a survey of possible accounts of praise’s nature. It then presents an overview of candidate norms governing our responses of praise. By attending to praise’s nature and appropriateness conditions, we stand to acquire a richer conception of what it is to be, and to regard (...) another as, a responsible agent. (shrink)

According to the thesis of doxastic wronging, our beliefs can non-derivatively wrong others. A recent criticism of this view claims that proponents of the doxastic wronging thesis have no principled grounds for denying that credences can likewise non-derivatively wrong, so they must countenance pervasive conflicts between morality and epistemic rationality. This paper defends the thesis of doxastic wronging from this objection by arguing that belief bears distinctive relationships to inquiry and blame that can explain why beliefs, but not credences, can (...) non-derivatively wrong. First, forming a belief (but not updating one’s credence) closes inquiry, and suspending judgment (but not updating one’s credence) opens inquiry. Consequently, beliefs can distinctively wrong others by prematurely closing inquiry or inappropriately opening inquiry. Second, beliefs (but not credences) can constitute blame. Unfittingly blaming someone can wrong them, and hence beliefs which constitute unfitting blame can distinctively wrong. In addition to defending the claim that only beliefs can non-derivatively wrong, this paper gestures towards an ethics of belief which attends to the relationship between belief and attitudes such as inquiry, faith, trust, and blame. (shrink)

I defend the thesis that legal standards of proof are reducible to thresholds of probability. Many have rejected this thesis because it seems to entail that defendants can be found liable solely on the basis of statistical evidence. I argue that this inference is invalid. I do so by developing a view, called Legal Causalism, that combines Thomson's (1986) causal analysis of evidence with recent work in formal theories of causal inference. On this view, legal standards of proof can be (...) reduced to probabilities, but deriving these probabilities involves more than just statistics. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...) who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

In this paper, I defend an epistemic requirement on fitting hopes and worries: it is fitting to hope or to worry that p only if one’s epistemic position makes it rational to suspend judgment as to whether p. This view, unlike prominent alternatives, is ecumenical; it retains its plausibility against a variety of different background views of epistemology. It also has other important theoretical virtues: it is illuminating, elegant, and extensionally adequate. Fallibilists about knowledge have special reason to be friendly (...) to my view; it can help them explain why it can be unfitting to hold on to hope and worry in the face of overwhelming evidence, and it can also help them explain the sense in which knowledge that p and hope that –p are in tension with one another. (shrink)

This paper introduces a novel form of pragmatic encroachment: one that makes a difference to the status of emotion rather than the status of belief. I begin by isolating a distinctive standard in terms of which we can evaluate emotion – one sometimes called “subjective fittingness,” “epistemic justification,” or “warrant.” I then show how this standard for emotion could face a kind of pragmatic encroachment importantly similar to the more familiar encroachment on epistemic standards for belief. Encroachment on warranted emotion (...) is a striking proposal that deserves attention. In fact, there are good reasons to think that encroachment on warranted emotion deserves to be considered the default view for those who already accept pragmatic encroachment on the epistemic status of belief. I support this parity claim by arguing for a principle that establishes a limited coordination between the conditions that warrant emotion and the conditions that justify belief. (shrink)

Philosophical Issues, Volume 31, Issue 1, Page 85-103, October 2021.

The rapidly increasing role of automation throughout the economy, culture and our personal lives has generated a large literature on the risks of algorithmic decision-making, particularly in high-stakes legal settings. Algorithmic tools are charged with bias, shrouded in secrecy, and frequently difficult to interpret. However, these criticisms have tended to focus on particular implementations, specific predictive techniques, and the idiosyncrasies of the American legal-regulatory regime. They do not address the more fundamental unease about the prospect that we might one day (...) replace judges with algorithms, no matter how fair, transparent, and intelligible they become. The aim of this paper is to propose an account of the source of that unease, and to evaluate its plausibility. I trace foundational unease with algorithmic decision-making in the law to the powerful intuition that there is a basic moral and legal difference between showing that something is true of many people just like you and showing that it is true of you. Human judgment attends to the exception; automation insists on blindly applying the rule. I show how this intuitive thought is connected to both epistemological arguments about the value of statistical evidence, as well as to court-centered conceptions of the rule of law. Unease with algorithmic decision-making in the law thus draws on an intuitive principle that underpins a disparate range of views in legal philosophy. This suggests the principle is deeply ingrained. Nonetheless, I argue that the powerful intuition is not as decisive as it may seem, and indeed runs into significant epistemological and normative challenges. At an epistemological level, I show how concerns about statistical evidence's ability to track the truth can be resolved by adopting a probabilistic, rather than modal, conception of truth-tracking. At a normative level, commitment to highly individualized decision-making co-exists with equally ingrained and competing principles, such as consistent application of law. This suggests that the “rule of law” may not identify a discrete set of institutional arrangements, as proponents of a court-centric conception would have it, but rather a more loosely defined set of values that could potentially be operationalized in multiple ways, including through some level of algorithmic adjudication. Although the prospect of replacing judges with algorithms is indeed unsettling, it does not necessarily entail unreasonable verdicts or an attack on the rule of law. (shrink)