This paper explores the idea that it is instrumentally valuable to learn normative truths. We consider an argument for "normative hedging" based on this principle, and examine the structure of decision-making under moral uncertainty that arises from it. But it also turns out that the value of normative information is inconsistent with the principle that learning *empirical* truths is instrumentally valuable. We conclude with a brief comment on "metanormative regress.".

This paper presents a unifying diagnosis of a number of important problems facing existing models of rational choice under moral uncertainty and proposes a remedy. I argue that the problems of (i) severely limited scope, (ii) intertheoretic comparisons, and (iii) 'swamping’ all stem from the way in which values are assigned to options in decision rules such as Maximisation of Expected Choiceworthiness. By assigning values to options under a given moral theory by asking something like ‘how much do I desire (...) this option, supposing this theory is true?’ rather than ‘how much value does this theory assign to this option?’ these problems can be avoided, while the appealing features of these accounts can be preserved. This amendment provides a role for the preferences, desires, or goals of rational agents that is curiously absent from the existing discussion of what individuals rationally ought to do when they are uncertain about what they morally ought to do. (shrink)

Automated decision making for sentencing is the use of a software algorithm to analyse a convicted offender’s case and deliver a sentence. This chapter reviews the moral arguments for and against employing automated decision making for sentencing and finds that its use is in principle morally permissible. Specifically, it argues that well-designed automated decision making for sentencing will better approximate the just sentence than human sentencers. Moreover, it dismisses common concerns about transparency, privacy and bias as unpersuasive or inapplicable. The (...) chapter also notes that moral disagreement about theories of just sentencing are plausibly resolved by applying the principle of maximising expected moral choiceworthiness, and that automated decision making is better suited to the resulting ensemble model. Finally, the chapter considers the challenge posed by penal populism. The dispiriting conclusion is that although it is in theory morally desirable to use automated decision-making for criminal sentencing, it may well be the case that we ought not to try. (shrink)

ABSTRACT Is uncertainty about preferences rationally possible? And if so, does it matter for rational decision? It is argued that uncertainty about preferences is possible and should play the same role in rational decision-making as uncertainty about worldly facts. The paper develops this hypothesis and defends it against various objections.