The application of artificial intelligence (AI) to judicial decision-making has already begun in many jurisdictions around the world. While AI seems to promise greater fairness, access to justice, and legal certainty, issues of discrimination and transparency have emerged and put liberal democratic principles under pressure, most notably in the context of bail decisions. Despite this, there has been no systematic analysis of the risks to liberal democratic values from implementing AI into judicial decision-making. This article sets out to fill this (...) void by identifying and engaging with challenges arising from artificial judicial decision-making, focusing on three pillars of liberal democracy, namely equal treatment of citizens, transparency, and judicial independence. Methodologically, the work takes a comparative perspective between human and artificial decision-making, using the former as a normative benchmark to evaluate the latter. -/- The chapter first argues that AI that would improve on equal treatment of citizens has already been developed, but not yet adopted. Second, while the lack of transparency in AI decision-making poses severe risks which ought to be addressed, AI can also increase the transparency of options and trade-offs that policy makers face when considering the consequences of artificial judicial decision-making. Such transparency of options offers tremendous benefits from a democratic perspective. Third, the overall shift of power from human intuition to advanced AI may threaten judicial independence, and with it the separation of powers. While improvements regarding discrimination and transparency are available or on the horizon, it remains unclear how judicial independence can be protected, especially with the potential development of advanced artificial judicial intelligence (AAJI). Working out the political and legal infrastructure to reap the fruits of artificial judicial intelligence in a safe and stable manner should become a priority of future research in this area. (shrink)

A straightforward way to handle moral uncertainty is simply to follow the moral theory in which you have most credence. This approach is known as My Favourite Theory. In this paper, I argue that, in some cases, My Favourite Theory prescribes choices that are, sequentially, worse in expected moral value than the opposite choices according to each moral theory you have any credence in. In addition this, problem generalizes to other approaches that avoid intertheoretic comparisons of value, such as My (...) Favourite Option, the Borda Rule, Variance Normalization, and the Principle of Maximizing Expected Normalized Moral Value. (shrink)

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.".

According to ‘Excluders’, descriptive uncertainty – but not normative uncertainty – matters to what we ought to do. Recently, several authors have argued that those wishing to treat normative uncertainty differently from descriptive uncertainty face a dependence problem because one's descriptive uncertainty can depend on one's normative uncertainty. The aim of this paper is to determine whether the phenomenon of dependence poses a decisive problem for Excluders. I argue that existing arguments fail to show this, and that, while stronger ones (...) can be found, Excluders can escape them. (shrink)

Effective Altruism is a rapidly growing and influential contemporary philosophical movement committed to updating utilitarianism in both theory and practice. The movement focuses on identifying urgent but neglected causes and inspiring supererogatory giving to meet the need. It also tries to build a broader coalition by adopting a more ecumenical approach to ethics which recognizes a wide range of values and moral constraints. These interesting developments distinguish Effective Altruism from the utilitarianism of the past in ways that invite cooperation and (...) warrant a fresh look from Thomists. Nonetheless Effective Altruism’s fundamentally consequentialist and aggregative model for ethics precludes more foundational agreement with Thomistic ethics in ways that limit the extent of practical cooperation. (shrink)

In many situations, people are unsure in their moral judgements. In much recent philosophical literature, this kind of moral doubt has been analysed in terms of uncertainty in one’s moral beliefs. Non-cognitivists, however, argue that moral judgements express a kind of conative attitude, more akin to a desire than a belief. This paper presents a scientifically informed reconciliation of non-cognitivism and moral doubt. The central claim is that attitudinal ambivalence—the degree to which one holds conflicting attitudes towards the same object—can (...) play the role of moral doubt for non-cognitivists. I will demonstrate that ambivalence has all of the features that we would expect it to have in order to play the role of moral doubt. It is gradable, can vary through time, covaries with strength of motivation, and is suitably distinct from the other features of our moral judgements. As well as providing a defence of non-cognitivism, this insight poses a new challenge for the view: deciding how to act under moral ambivalence. (shrink)

Morally speaking, what should one do when one is morally uncertain? Call this the Moral Uncertainty Question. In this paper, I argue that a non-ideal moral theory provides the best answer to the Moral Uncertainty Question. I begin by arguing for a strong ought-implies-can principle---morally ought implies agentially can---and use that principle to clarify the structure of a compelling non-ideal moral theory. I then describe the ways in which one's moral uncertainty affects one's moral prescriptions: moral uncertainty constrains the set (...) of moral prescriptions one is subject to, and at the same time generates new non-ideal moral reasons for action. I end by surveying the problems that plague alternative answers to the Moral Uncertainty Question, and show that my preferred answer avoids most of those problems. (shrink)

Suppose that an altruistic agent who is uncertain between evidential and causal decision theory finds herself in a situation where these theories give conflicting verdicts. We argue that even if she has significantly higher credence in CDT, she should nevertheless act in accordance with EDT. First, we claim that the appropriate response to normative uncertainty is to hedge one's bets. That is, if the stakes are much higher on one theory than another, and the credences you assign to each of (...) these theories are not very different, then it is appropriate to choose the option that performs best on the high-stakes theory. Second, we show that, given the assumption of altruism, the existence of correlated decision makers will increase the stakes for EDT but leave the stakes for CDT unaffected. Together these two claims imply that whenever there are sufficiently many correlated agents, the appropriate response is to act in accordance with EDT. (shrink)

While ordinary decision theory focuses on empirical uncertainty, real decision-makers also face normative uncertainty: uncertainty about value itself. From a purely formal perspective, normative uncertainty is comparable to (Harsanyian or Rawlsian) identity uncertainty in the 'original position', where one's future values are unknown. A comprehensive decision theory must address twofold uncertainty -- normative and empirical. We present a simple model of twofold uncertainty, and show that the most popular decision principle -- maximising expected value (`Expectationalism') -- has different formulations, namely (...) Ex-Ante Expectationalism, Ex-Post Expectationalism, and hybrid theories. These alternative theories recommend different decisions, reasoning modes, and attitudes to risk. But they converge under an interesting (necessary and sufficient) condition. (shrink)

Decision-making under normative uncertainty requires an agent to aggregate the assessments of options given by rival normative theories into a single assessment that tells her what to do in light of her uncertainty. But what if the assessments of rival theories differ not just in their content but in their structure -- e.g., some are merely ordinal while others are cardinal? This paper describes and evaluates three general approaches to this "problem of structural diversity": structural enrichment, structural depletion, and multi-stage (...) aggregation. All three approaches have notable drawbacks, but I tentatively defend multi-stage aggregation as least bad of the three. (shrink)

What kinds of fundamental limits are there in how capable artificial intelligence (AI) systems might become? Two questions in particular are of interest: (1) How much more capable could AI become relative to humans, and (2) how easily could superhuman capability be acquired? To answer these questions, we will consider the literature on human expertise and intelligence, discuss its relevance for AI, and consider how AI could improve on humans in two major aspects of thought and expertise, namely simulation and (...) pattern recognition. We find that although there are very real limits to prediction, it seems like AI could still substantially improve on human intelligence. (shrink)

How should you decide what to do when you're uncertain about basic normative principles (e.g., Kantianism vs. utilitarianism)? A natural suggestion is to follow some "second-order" norm: e.g., "comply with the first-order norm you regard as most probable" or "maximize expected choiceworthiness". But what if you're uncertain about second-order norms too -- must you then invoke some third-order norm? If so, it seems that any norm-guided response to normative uncertainty is doomed to a vicious regress. In this paper, I aim (...) to rescue second-order norms from this threat of regress. I first elaborate and defend the suggestion some philosophers have entertained that the regress problem forces us to accept normative externalism, the view that at least one norm is incumbent on agents regardless of their beliefs or evidence concerning that norm. But, I then argue, we need not accept externalism about first-order (e.g., moral) norms, thus closing off any question of what an agent should do in light of her normative beliefs. Rather, it is more plausible to ascribe external force to a single, second-order rational norm: the enkratic principle, correctly formulated. This modest form of externalism, I argue, is both intrinsically well-motivated and sufficient to head off the threat of regress. (shrink)

This essay explains why there are good reasons to practice philosophy as a way of life. The argument begins with the assumption that we should live well but that our understanding of how to live well can be mistaken. Philosophical reason and reflection can help correct these mistakes. Nonetheless, the evidence suggests that philosophical reasoning often fails to change our dispositions and behavior. Drawing on the work of Pierre Hadot, the essay claims that spiritual exercises and communal engagement mitigate the (...) factors that prevent us from living in accord- ance with our conceptions of the good life. So, many of us have reasons to engage in philosophical reasoning along with behavioral, cognitive, and social strategies to alter our behavior and attitudes so that they’re in line with our philosophical commitments. In these respects, many of us should practice philosophy as a way of life. (shrink)

In ‘Normative Uncertainty as a Voting Problem’, William MacAskill argues that positive credence in ordinal-structured or intertheoretically incomparable normative theories does not prevent an agent from rationally accounting for her normative uncertainties in practical deliberation. Rather, such an agent can aggregate the theories in which she has positive credence by methods borrowed from voting theory—specifically, MacAskill suggests, by a kind of weighted Borda count. The appeal to voting methods opens up a promising new avenue for theories of rational choice under (...) normative uncertainty. The Borda rule, however, is open to at least two serious objections. First, it seems implicitly to ‘cardinalize’ ordinal theories, and so does not fully face up to the problem of merely ordinal theories. Second, the Borda rule faces a problem of option individuation. MacAskill attempts to solve this problem by invoking a measure on the set of practical options. But it is unclear that there is any natural way of defining such a measure that will not make the output of the Borda rule implausibly sensitive to irrelevant empirical features of decision-situations. After developing these objections, I suggest an alternative: the McKelvey uncovered set, a Condorcet method that selects all and only the maximal options under a strong pairwise defeat relation. This decision rule has several advantages over Borda and mostly avoids the force of MacAskill’s objection to Condorcet methods in general. (shrink)

Given the deep disagreement surrounding population axiology, one should remain uncertain about which theory is best. However, this uncertainty need not leave one neutral about which acts are better or worse. We show that, as the number of lives at stake grows, the Expected Moral Value approach to axiological uncertainty systematically pushes one toward choosing the option preferred by the Total View and critical-level views, even if one’s credence in those theories is low.

The principle that rational agents should maximize expected utility or choiceworthiness is intuitively plausible in many ordinary cases of decision-making under uncertainty. But it is less plausible in cases of extreme, low-probability risk (like Pascal's Mugging), and intolerably paradoxical in cases like the St. Petersburg and Pasadena games. In this paper I show that, under certain conditions, stochastic dominance reasoning can capture most of the plausible implications of expectational reasoning while avoiding most of its pitfalls. Specifically, given sufficient background uncertainty (...) about the choiceworthiness of one's options, many expectation-maximizing gambles that do not stochastically dominate their alternatives "in a vacuum" become stochastically dominant in virtue of that background uncertainty. But, even under these conditions, stochastic dominance will not require agents to accept options whose expectational superiority depends on sufficiently small probabilities of extreme payoffs. The sort of background uncertainty on which these results depend looks unavoidable for any agent who measures the choiceworthiness of her options in part by the total amount of value in the resulting world. At least for such agents, then, stochastic dominance offers a plausible general principle of choice under uncertainty that can explain more of the apparent rational constraints on such choices than has previously been recognized. (shrink)

In the growing literature on decision-making under moral uncertainty, a number of skeptics have argued that there is an insuperable barrier to rational "hedging" for the risk of moral error, namely the apparent incomparability of moral reasons given by rival theories like Kantianism and utilitarianism. Various general theories of intertheoretic value comparison have been proposed to meet this objection, but each suffers from apparently fatal flaws. In this paper, I propose a more modest approach that aims to identify classes of (...) moral theories that share common principles strong enough to establish bases for intertheoretic comparison. I show that, contra the claims of skeptics, there are often rationally perspicuous grounds for precise, quantitative value comparisons within such classes. In light of this fact, I argue, the existence of some apparent incomparabilities between widely divergent moral theories cannot serve as a general argument against hedging for one's moral uncertainties. (shrink)

Defenders of deontological constraints in normative ethics face a challenge: how should an agent decide what to do when she is uncertain whether some course of action would violate a constraint? The most common response to this challenge has been to defend a threshold principle on which it is subjectively permissible to act iff the agent's credence that her action would be constraint-violating is below some threshold t. But the threshold approach seems arbitrary and unmotivated: what would possibly determine where (...) the threshold should be set, and why should there be any precise threshold at all? Threshold views also seem to violate ought agglomeration, since a pair of actions each of which is below the threshold for acceptable moral risk can, in combination, exceed that threshold. In this paper, I argue that stochastic dominance reasoning can vindicate and lend rigor to the threshold approach: given characteristically deontological assumptions about the moral value of acts, it turns out that morally safe options will stochastically dominate morally risky alternatives when and only when the likelihood that the risky option violates a moral constraint is greater than some precisely definable threshold (in the simplest case, .5). I also show how, in combination with the observation that deontological moral evaluation is relativized to particular choice situations, this approach can overcome the agglomeration problem. This allows the deontologist to give a precise and well-motivated response to the problem of uncertainty. (shrink)

The development of artificial intelligence will require systems of ethical decision making to be adapted for automatic computation. However, projects to implement moral reasoning in artificial moral agents so far have failed to satisfactorily address the widespread disagreement between competing approaches to moral philosophy. In this paper I argue that the proper response to this situation is to design machines to be fundamentally uncertain about morality. I describe a computational framework for doing so and show that it efficiently resolves common (...) obstacles to the implementation of moral philosophy in intelligent machines. (shrink)

How should an agent decide what to do when she is uncertain not just about morally relevant empirical matters, like the consequences of some course of action, but about the basic principles of morality itself? This question has only recently been taken up in a systematic way by philosophers. Advocates of moral hedging claim that an agent should weigh the reasons put forward by each moral theory in which she has positive credence, considering both the likelihood that that theory is (...) true and the strength of the reasons it posits. The view that it is sometimes rational to hedge for one's moral uncertainties, however, has recently come under attack both from those who believe that an agent should always be guided by the dictates of the single moral theory she deems most probable and from those who believe that an agent's moral beliefs are simply irrelevant to what she ought to do. Among the many objections to hedging that have been pressed in the recent literature is the worry that there is no non-arbitrary way of making the intertheoretic comparisons of moral value necessary to aggregate the value assignments of rival moral theories into a single ranking of an agent's options. -/- This dissertation has two principal objectives: First, I argue that, contra these recent objections, an agent's moral beliefs and uncertainties are relevant to what she rationally ought to do, and more particularly, that agents are at least sometimes rationally required to hedge for their moral uncertainties. My principal argument for these claims appeals to the enkratic conception of rationality, according to which the requirements of practical rationality derive from an agent's beliefs about the objective, desire-independent value or choiceworthiness of her options. Second, I outline a new general theory of rational choice under moral uncertainty. Central to this theory is the idea of content-based aggregation, that the principles according to which an agent should compare and aggregate rival moral theories are grounded in the content of those theories themselves, including not only their value assignments but also the metaethical and other non-surface-level propositions that underlie, justify, or explain those value assignments. (shrink)

Given the deep disagreement surrounding population axiology, one should remain uncertain about which theory is best. However, this uncertainty need not leave one neutral about which acts are better or worse. We show that as the number of lives at stake grows, the Expected Moral Value approach to axiological uncertainty systematically pushes one towards choosing the option preferred by the Total and Critical Level views, even if one’s credence in those theories is low.

We sometimes seem to face fundamental moral uncertainty, i.e., uncertainty about what is morally good or morally right that cannot be reduced to ordinary descriptive uncertainty. This phenomenon raises a puzzle for noncognitivism, according to which moral judgments are desire-like attitudes as opposed to belief-like attitudes. Can a state of moral uncertainty really be a noncognitive state? So far, noncognitivists have not been able to offer a completely satisfactory account. Here, we argue that noncognitivists should exploit the formal analogy between (...) moral uncertainty and moral multi-objective decision problems. Our ‘multi-objective story’ enables noncognitivists to save our moral uncertainty thought and talk by explaining how the underlying phenomenon could be noncognitive. (shrink)

As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of (...) classes with distinct moral preferences and that this codification can be used to express moral uncertainty of an AI. Choice analysis allows for the identification of classes and their moral preferences based on observed choice data. Our reformulation of the metanormative framework is theory-rooted and practical in the sense that it avoids runtime issues in real time applications. To illustrate our approach we conceptualize a society in which AI Systems are in charge of making policy choices. While one of the systems uses a baseline morally certain model, the other uses a morally uncertain model. We highlight cases in which the AI Systems disagree about the policy to be chosen, thus illustrating the need to capture moral uncertainty in AI systems. (shrink)

We’re often unsure what morality requires, but we need to act anyway. There is a growing philosophical literature on how to navigate moral uncertainty. But much of it asks how to rationally pursue the goal of acting morally, using decision-theoretic models to address that question. I argue that using these popular approaches leaves some central and pressing questions about moral uncertainty unaddressed. To help us make sense of experiences of moral uncertainty, we should shift away from focusing on what it’s (...) rational to do when facing moral uncertainty, and instead look directly at what it’s moral to do about moral uncertainty—for example, how risk averse we morally ought to be, or which personal sacrifices we’re morally obligated to make in order to reduce our risk of moral wrongdoing. And orthodox, expectation-maximizing, decision-theoretic models aren’t well-suited to this task—in part because they presuppose the answers to some important moral questions. For example, if approaching moral uncertainty in a moral way requires us to “maximize expected moral rightness,” that’s, itself, a contentious claim about the demands of morality—one that requires significant moral argument, and that I ultimately suggest is mistaken. Of course, it’s possible to opt, instead, for a variety of alternative decision-theoretic models. But, in order to choose between proposed decision-theoretic models, and select one that is well-suited to handling these cases, we first would need to settle more foundational, moral questions—about, for example, what we should be willing to give up in order to reduce the risk that we’re acting wrongly. Decision theory may be able to formalize the conclusions of these deliberations, but it is not a substitute for them, and it won’t be able to settle the right answers in advance. For now, when we discuss moral uncertainty, we need to wade directly into moral debate, without the aid of decision theory’s formalism. (shrink)

According to the traditional Bayesian view of credence, its structure is that of precise probability, its objects are descriptive propositions about the empirical world, and its dynamics are given by conditionalization. Each of the three essays that make up this thesis deals with a different variation on this traditional picture. The first variation replaces precise probability with sets of probabilities. The resulting imprecise Bayesianism is sometimes motivated on the grounds that our beliefs should not be more precise than the evidence (...) calls for. One known problem for this evidentially motivated imprecise view is that in certain cases, our imprecise credence in a particular proposition will remain the same no matter how much evidence we receive. In the first essay I argue that the problem is much more general than has been appreciated so far, and that it’s difficult to avoid without compromising the initial evidentialist motivation. The second variation replaces descriptive claims with moral claims as the objects of credence. I consider three standard arguments for probabilism with respect to descriptive uncertainty—representation theorem arguments, Dutch book arguments, and accuracy arguments—in order to examine whether such arguments can also be used to establish probabilism with respect to moral uncertainty. In the second essay, I argue that by and large they can, with some caveats. First, I don’t examine whether these arguments can be given sound non-cognitivist readings, and any conclusions therefore only hold conditional on cognitivism. Second, decision-theoretic representation theorems are found to be less convincing in the moral case, because there they implausibly commit us to thinking that intertheoretic comparisons of value are always possible. Third and finally, certain considerations may lead one to think that imprecise probabilism provides a more plausible model of moral epistemology. The third variation considers whether, in addition to conditionalization, agents may also change their minds by becoming aware of propositions they had not previously entertained, and therefore not previously assigned any probability. More specifically, I argue that if we wish to make room for reflective equilibrium in a probabilistic moral epistemology, we must allow for awareness growth. In the third essay, I sketch the outline of such a Bayesian account of reflective equilibrium. Given that this account gives a central place to awareness growth, and that the rationality constraints on belief change by awareness growth are much weaker than those on belief change by conditionalization, it follows that the rationality constraints on the credences of agents who are seeking reflective equilibrium are correspondingly weaker. (shrink)

Recenze knihy:Toby Ord, The Precipice. Existential Risk and the Future of Humanity. Bloomsbury Publishing, 2020, 480 s.

Rohana faces a choice where she can produce either a better outcome by lying or a worse outcome by telling the truth. She justifiably, but falsely, believes in.

Volume 98, Issue 4, December 2020, Page 747-762.

When we are faced with a choice among acts, but are uncertain about the true state of the world, we may be uncertain about the acts’ “choiceworthiness”. Decision theories guide our choice by making normative claims about how we should respond to this uncertainty. If we are unsure which decision theory is correct, however, we may remain unsure of what we ought to do. Given this decision-theoretic uncertainty, meta-theories attempt to resolve the conflicts between our decision theories...but we may be (...) unsure which meta-theory is correct as well. This reasoning can launch a regress of ever-higher-order uncertainty, which may leave one forever uncertain about what one ought to do. There is, fortunately, a class of circumstances under which this regress is not a problem. If one holds a cardinal understanding of subjective choiceworthiness, and accepts certain other criteria, one’s hierarchy of metanormative uncertainty ultimately converges to precise definitions of “subjective choiceworthiness” for any finite set of acts. If one allows the metanormative regress to extend to the transfinite ordinals, the convergence criteria can be weakened further. Finally, the structure of these results applies straightforwardly not just to decision-theoretic uncertainty, but also to other varieties of normative uncertainty, such as moral uncertainty. (shrink)

Even if I think it very likely that some morally good act is supererogatory rather than obligatory, I may nonetheless be rationally required to perform that act. This claim follows from an apparently straightforward dominance argument, which parallels Jacob Ross's argument for 'rejecting' moral nihilism. These arguments face analogous pairs of objections that illustrate general challenges for dominance reasoning under normative uncertainty, but (I argue) these objections can be largely overcome. This has practical consequences for the ethics of philanthropy -- (...) in particular, it means that donors are often rationally required to maximize the positive impact of their donations. (shrink)

If an agent is unsure about which moral theory or principle should guide her action in a decision situation, she faces moral uncertainty. In recent years, various strategies have been explored to deal with this type of uncertainty. In this paper, we briefly mention two strategies from the literature that make use of credence distributions over moral theories, namely “my favourite theory” and “maximizing expected choice-worthiness”. As an alternative, we propose a two-step procedure which uses the concept of aggregation over (...) structural properties. It is standard in the theory of collective choice and has recently been applied to the Kuhnian problem of theory choice as well. The idea is to explore how a morally-motivated rational agent may assess different moral theories on the basis of fundamental properties. These properties are ranked on a common scale of qualitative verdicts. This method enables an outside observer to make comparisons across competing moral theories and then conclude to what degree these theories fulfil a set of postulated properties. By doing so, we try to render the reasons more transparent that lie behind different types of credence ascriptions. (shrink)

Metanormativists hold that moral uncertainty can affect how we ought, in some morally authoritative sense, to act. Many metanormativists aim to generalize expected utility theory for normative uncertainty. Such accounts face the “easy problem of intertheoretic comparisons”: the worry that distinct theories’ assessments of choiceworthiness are incomparable. The easy problem may well be resolvable, but another problem looms: while some moral theories assign cardinal degrees of choiceworthiness, other theories’ choiceworthiness assignments are merely ordinal. Expected choiceworthiness over such theories is undefined. (...) Call this the “hard problem of intertheoretic comparisons.” This paper argues that to solve the hard problem, we should model moral theories with imprecise choiceworthiness. Imprecise choiceworthiness assignments can model incomplete cardinal information about choiceworthiness, with precise cardinal choiceworthiness and merely ordinal choiceworthiness as limiting cases. Generalizing familiar decision theories for imprecise choiceworthiness to the case of moral uncertainty generates puzzles, however: natural generalizations seem to require reifying parts of the model that don’t correspond to anything in normative reality. I discuss three ways of addressing this problem: by demystifying the reified elements by using them as promiscuously as possible; by constructing alternative decision theories that don’t require the troublesome elements; and by employing an alternative model of metanormative decision problems, and of moral uncertainty generally. (shrink)