Boris Babic

This paper discusses a novel problem at the intersection of ethics and epistemology: there can be cases in which moral considerations seem to "encroach'' upon belief from multiple directions at once, and possibly to varying degrees, thereby leaving their overall effect on belief unclear. We introduce these cases -- cases of moral encroachment under moral uncertainty -- and show that they pose a problem for all predominant accounts of moral encroachment. We then address the problem, by developing a modular Bayesian framework that, we argue, is sufficiently flexible and scaleable to accommodate the multifaceted uncertainty we describe while still generating clear recommendations for an agent's beliefs. Our framework has several practical upshots, and we close by articulating them: we derive insights about the relationship between moral character and doxastic behavior and make suggestions for how to encourage people to revise their doxastic states in morally laudable ways, without deviating from core Bayesian norms.

In this paper we develop a general theory of algorithmic fairness. Drawing on Johnson King and Babic’s work on moral encroachment, on Gary Becker’s work on labor market discrimination, and on Strawson’s idea of resentment and indignation as responses to violations of the demand for goodwill toward oneself and others, we locate attitudes to fairness in an agent’s utility function. In particular, we first argue that fairness is a matter of a decision-maker’s relative concern for the plight of people from different groups, rather than of the outcomes produced for different groups. We then show how an agent’s preferences, including in particular their attitudes to error, give rise to their decision thresholds. Tying these points together, we argue that the agent’s relative degrees of concern for different groups manifest in a difference in decision thresholds applied to these groups.

Many philosophers have argued that statistical evidence regarding group characteristics (particularly stereotypical ones) can create normative conflicts between the requirements of epistemic rationality and our moral obligations to each other. In a recent article, Johnson-King and Babic argue that such conflicts can usually be avoided: what ordinary morality requires, they argue, epistemic rationality permits. In this article, we show that as data get large, Johnson-King and Babic’s approach becomes less plausible. More constructively, we build on their project and develop a generalized model of reasoning about stereotypes under which one can indeed avoid normative conflicts, even in a big data world, when data contain some noise. In doing so, we also articulate a general approach to rational belief updating for noisy data.

My goal in this dissertation is to start a conversation about the role of risk in the decision-theoretic assessment of partial beliefs or credences in formal epistemology. I propose a general theory of epistemic risk in terms of relative sensitivity to different types of graded error. The approach I develop is broadly inspired by the pragmatism of the American philosopher Charles Sanders Peirce and his notion of the ``economy of research.'' I express this framework in information-theoretic terms and show that epistemic risk, so understood, is dual to information entropy. As a result, every unit increase in risk comes with a corresponding unit decrease in information entropy and epistemic risk may be expressed in terms of entropic change. I explain the significance of this for the selection of priors and the Laplacian principle of indifference. I also extend this notion of epistemic risk to the assessment of updating rules, where a similar duality between risk and information holds. In the dynamic context, epistemic risk is given by cross-entropic change. Here I explore the relationship between risk, the Value of Knowledge Theorem, dynamic coherence, and the role of expected accuracy in the selection of update rules. Finally, I apply these considerations to a social institution where attitudes to error are especially salient -- namely, legal decision-making -- and argue that considerations regarding the relative severity of different types of error are central to understanding evidentiary burdens of proof and the probative value of statistical evidence.

Approximate coherentism suggests that imperfectly rational agents should hold approximately coherent credences. This norm is intended as a generalization of ordinary coherence. I argue that it may be unable to play this role by considering its application under learning experiences. While it is unclear how imperfect agents should revise their beliefs, I suggest a plausible route is through Bayesian updating. However, Bayesian updating can take an incoherent agent from relatively more coherent credences to relatively less coherent credences, depending on the data observed. Thus, comparative rationality judgments among incoherent agents are unduly sensitive to luck.

I propose a general alethic theory of epistemic risk according to which the riskiness of an agent’s credence function encodes her relative sensitivity to different types of graded error. After motivating and mathematically developing this approach, I show that the epistemic risk function is a scaled reflection of expected inaccuracy. This duality between risk and information enables us to explore the relationship between attitudes to epistemic risk, the choice of scoring rules in epistemic utility theory, and the selection of priors in Bayesian epistemology more generally.