Data-driven, decision-making technologies used in the justice system to inform decisions about bail, parole, and prison sentencing are biased against historically marginalized groups (Angwin, Larson, Mattu, & Kirchner 2016). But these technologies’ judgments—which reproduce patterns of wrongful discrimination embedded in the historical datasets that they are trained on—are well-evidenced. This presents a puzzle: how can we account for the wrong these judgments engender without also indicting morally permissible statistical inferences about persons? I motivate this puzzle and attempt an answer.

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)

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain (...) why this systemic exclusion is of moral concern and to offer a solution to address it. (shrink)

Understanding Institutions proposes a new unified theory of social institutions that combines the best insights of philosophers and social scientists who have written on this topic. Francesco Guala presents a theory that combines the features of three influential views of institutions: as equilibria of strategic games, as regulative rules, and as constitutive rules. -/- Guala explains key institutions like money, private property, and marriage, and develops a much-needed unification of equilibrium- and rules-based approaches. Although he uses game theory concepts, the (...) theory is presented in a simple, clear style that is accessible to a wide audience of scholars working in different fields. Outlining and discussing various implications of the unified theory, Guala addresses venerable issues such as reflexivity, realism, Verstehen, and fallibilism in the social sciences. He also critically analyses the theory of “looping effects” and “interactive kinds” defended by Ian Hacking, and asks whether it is possible to draw a demarcation between social and natural science using the criteria of causal and ontological dependence. Focusing on current debates about the definition of marriage, Guala shows how these abstract philosophical issues have important practical and political consequences. -/- Moving beyond specific cases to general models and principles, Understanding Institutions offers new perspectives on what institutions are, how they work, and what they can do for us. (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)

This article provides the first large-scale, longitudinal study examining publication rates by gender in philosophy journals. We find that from 1900 to 1990 the proportion of women authorships in philosophy increased, but it has plateaued since the 1990s. Top Philosophy journals publish the lowest proportion of women, and anonymous review does not increase the proportion publishing in these journals. Value Theory journals do not publish articles by women in proportion to their presence in the subdiscipline. Although the proportion of women (...) authorships in philosophy has increased over time, measurable disparities persist. (shrink)