Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in which (...) such trajectories can be subject to evaluation in relation to their temporal dynamics, robustness, and representation. (shrink)

Previous edition, 1st, published in 1971.

We care not only about how people treat us, but also what they believe of us. If I believe that you’re a bad tipper given your race, I’ve wronged you. But, what if you are a bad tipper? It is commonly argued that the way racist beliefs wrong is that the racist believer either misrepresents reality, organizes facts in a misleading way that distorts the truth, or engages in fallacious reasoning. In this paper, I present a case that challenges this (...) orthodoxy: the case of the supposedly rational racist. We live in a world that has been, and continues to be, structured by racist attitudes and institutions. As a result, the evidence might be stacked in favour of racist beliefs. But, if there are racist beliefs that reflect reality and are rationally justified, what could be wrong with them? Moreover, how do I wrong you by believing what I epistemically ought believe given the evidence? To address this challenge, we must recognize that there are not only epistemic norms governing belief, but moral ones as well. This view, however, is at odds with the assumption that moral obligation requires a kind of voluntary control that we lack with regard to our beliefs. This background assumption motivates many philosophers to try to explain away the appearance that beliefs can wrong by locating the wrong elsewhere, e.g., in an agent’s actions. Further, even accounts that accept the thesis that racist beliefs can wrong restrict the class of beliefs that wrong to beliefs that are either false or the result of hot irrationality, e.g., the racist belief is a result of ill-will. In this paper I argue that although the these accounts will capture many of the wrongs associated with racist beliefs, they will be only partial explanations because they cannot explain the wrong committed by the supposedly rational racist. The challenge posed by the supposedly rational racist concerns our epistemic practices in a non-ideal world. The world is an unjust place, and there may be many morally objectionable beliefs it justifies. To address this challenge, we must seriously consider the thesis people wrong others in virtue of what they believe about them, and not just in virtue of what they do. (shrink)

Historical patterns of discrimination seem to present us with conflicts between what morality requires and what we epistemically ought to believe. I will argue that these cases lend support to the following nagging suspicion: that the epistemic standards governing belief are not independent of moral considerations. We can resolve these seeming conflicts by adopting a framework wherein standards of evidence for our beliefs to count as justified can shift according to the moral stakes. On this account, believing a paradigmatically racist (...) belief reflects a failure to not only attend to the epistemic risk of being wrong, but also a failure to attend to the distinctively moral risk of wronging others given what we believe. (shrink)

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Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems (...) requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems. (shrink)

Philosophy &Public Affairs, Volume 51, Issue 2, Page 166-190, Spring 2023.

This paper is about an overlooked aspect—the cognitive or epistemic aspect—of the moral demand we place on one another to be treated well. We care not only how people act towards us and what they say of us, but also what they believe of us. That we can feel hurt by what others believe of us suggests both that beliefs can wrong and that there is something we epistemically owe to each other. This proposal, however, surprises many theorists who claim (...) it lacks both intuitive and theoretical support. This paper argues that the proposal has intuitive support and is not at odds with much contemporary theorizing about what we owe to each other. (shrink)

We care what people think of us. The thesis that beliefs wrong, although compelling, can sound ridiculous. The norms that properly govern belief are plausibly epistemic norms such as truth, accuracy, and evidence. Moral and prudential norms seem to play no role in settling the question of whether to believe p, and they are irrelevant to answering the question of what you should believe. This leaves us with the question: can we wrong one another by virtue of what we believe (...) about each other? Can beliefs wrong? In this introduction, I present a brief summary of the articles that make up this special issue. The aim is to direct readers to open avenues for future research by highlighting questions and challenges that are far from being settled. These papers shouldn’t be taken as the last word on the subject. Rather, they mark the beginning of a serious exploration into a set of questions that concern the morality of belief, i.e., doxastic morality. (shrink)

Social groups—like teams, committees, gender groups, and racial groups—play a central role in our lives and in philosophical inquiry. Here I develop and motivate a structuralist ontology of social groups centered on social structures (i.e., networks of relations that are constitutively dependent on social factors). The view delivers a picture that encompasses a diverse range of social groups, while maintaining important metaphysical and normative distinctions between groups of different kinds. It also meets the constraint that not every arbitrary collection of (...) people is a social group. In addition, the framework provides resources for developing a broader structuralist view in social ontology. (shrink)

In this article, I develop a general explanatory model of the liability and lesser evil justifications of harm. Despite their respective provenance in consequentialist and deontological ethics, both justifications are, at root, rich forms of the proportionality relationship between a shared set of underlying normative variables. The nature of the proportionality relationship, and the conditions under which it operates, differ between the two forms of justification. The article explores these differences in detail and the implications they have for the justification (...) of self-defense and war. (shrink)

Machine learning algorithms pervade contemporary society. They are integral to social institutions, inform processes of governance, and animate the mundane technologies of daily life. Consistently, the outcomes of machine learning reflect, reproduce, and amplify structural inequalities. The field of fair machine learning has emerged in response, developing mathematical techniques that increase fairness based on anti-classification, classification parity, and calibration standards. In practice, these computational correctives invariably fall short, operating from an algorithmic idealism that does not, and cannot, address systemic, Intersectional (...) stratifications. Taking present fair machine learning methods as our point of departure, we suggest instead the notion and practice of algorithmic reparation. Rooted in theories of Intersectionality, reparative algorithms name, unmask, and undo allocative and representational harms as they materialize in sociotechnical form. We propose algorithmic reparation as a foundation for building, evaluating, adjusting, and when necessary, omitting and eradicating machine learning systems. (shrink)

Most philosophers find it puzzling how beliefs could wrong, and this leads them to conclude that they do not. So there is much philosophical work to be done in sorting out whether I am right to say that they do, as well as how this could be so. But in this paper I will take for granted that beliefs can wrong, and ask instead when beliefs wrong. My answer will be that beliefs wrong when they falsely diminish. This answer has (...) three parts: that beliefs wrong only when they are false, that beliefs wrong only when they diminish, and that false diminishment is sufficient for wronging. I will seek to elaborate on and defend all three of these claims, but it is the first to which I will give the most attention. (shrink)

This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently specified (...) substantive views about justice in outcome distributions. (shrink)

Increasing concerns have been raised regarding artificial intelligence (AI) bias, and in response, efforts have been made to pursue AI fairness. In this paper, we argue that the idea of structural injustice serves as a helpful framework for clarifying the ethical concerns surrounding AI bias—including the nature of its moral problem and the responsibility for addressing it—and reconceptualizing the approach to pursuing AI fairness. Using AI in healthcare as a case study, we argue that AI bias is a form of (...) structural injustice that exists when AI systems interact with other social factors to exacerbate existing social inequalities, making some groups of people more vulnerable to undeserved burdens while conferring unearned benefits to others. The goal of AI fairness, understood this way, is about pursuing a more just social structure with the development and usage of AI systems when appropriate. We further argue that all participating agents in the unjust social structure associated with AI bias bear a shared responsibility to join collective action with the goal of reforming the social structure, and we provide a list of practical recommendations for agents in various social positions to contribute to this collective action. (shrink)

You care about what others believe about you. What others believe about you determines whether you have a good reputation, whether you have the respect of your peers, and whether your friends genuinely like you. Your caring about others’ beliefs makes sense, because others’ beliefs bear directly upon your level of well-being. Your beliefs can influence others’ well-being, as much as their beliefs can influence yours. How your beliefs influence another’s well-being is a different matter from whether your beliefs are (...) supported by the evidence. Sometimes you can benefit another person by regulating your beliefs in response to considerations of her well-being, not of the evidence. Usually, you do not have strong reasons to regulate your beliefs in response to considerations of the interests of others. But it can be different when the person in question is your friend. Within some perfectly good friendships, the support that each friend provides for the other extends as far as their being willing to regulate their beliefs with the goal of benefiting the other, even if that makes it less likely that their beliefs will be supported by the evidence. Within our friendships, we then have reasons for belief that do not arise directly from, and sometimes compete with, reasons provided by evidence. This claim conflicts with the widespread Aristotelian view that good friendship is oriented to virtue. But it fits with a more plausible view about friendship, on which the function of friendship is to help us cope with the fact that we are not fully virtuous, and to serve needs that we have because we are not fully virtuous. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false (...) positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three (...) key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns. (shrink)

Philosophy & Public Affairs, Volume 50, Issue 2, Page 239-266, Spring 2022.

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...) in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)

In the Book of Common Prayer’s Rite II version of the Eucharist, the congregation confesses, “we have sinned against you in thought, word, and deed”. According to this confession we wrong God not just by what we do and what we say, but also by what we think. The idea that we can wrong someone not just by what we do, but by what think or what we believe, is a natural one. It is the kind of wrong we feel (...) when those we love believe the worst about us. And it is one of the salient wrongs of racism and sexism. Yet it is puzzling to many philosophers how we could wrong one another by virtue of what we believe about them. This paper defends the idea that we can morally wrong one another by what we believe about them from two such puzzles. The first puzzle concerns whether we have the right sort of control over our beliefs for them to be subject to moral evaluation. And the second concerns whether moral wrongs would come into conflict with the distinctively epistemic standards that govern belief. Our answer to both puzzles is that the distinctively epistemic standards governing belief are not independent of moral considerations. This account of moral encroachment explains how epistemic norms governing belief are sensitive to the moral requirements governing belief. (shrink)

In this paper, I argue that morality might bear on belief in at least two conceptually distinct ways. The first is that morality might bear on belief by bearing on questions of justification. The claim that it does is the doctrine of moral encroachment. The second, is that morality might bear on belief given the central role belief plays in mediating and thereby constituting our relationships with one another. The claim that it does is the doctrine of doxastic wronging. Though (...) conceptually distinct, the two doctrines overlap in important ways. This paper provides clarification on the relationship between the two, providing reasons throughout that we should accept both. (shrink)