This is an important book precisely because there is none other quite like it.

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.

Richard Rudner famously argues that the communication of scientific advice to policy makers involves ethical value judgments. His argument has, however, been rightly criticized. This article revives Rudner’s conclusion, by strengthening both his lines of argument: we generalize his initial assumption regarding the form in which scientists must communicate their results and complete his ‘backup’ argument by appealing to the difference between private and public decisions. Our conclusion that science advisors must, for deep-seated pragmatic reasons, make value judgments is further (...) bolstered by reflections on how the scientific contribution to policy is far less straightforward than the Rudner-style model suggests. (shrink)

The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

We commonly evaluate search engines and the results they return, but what grounds those evaluations? One straightforward way of evaluating search engines appeals to their ability to satisfy the goals of the user. Are there, in addition, user-independent norms, that allow us to evaluate search engines in ways that may come apart from their ability to satisfy the individuals who use them? One way of grounding such norms appeals to moral or political considerations. I argue that in addition to those (...) norms, there are also distinct user-independent epistemic norms that apply to search engines. Evaluating search engines relative to them, however, requires us to appreciate the impact search engines have on our practices and norms of inquiry more broadly: by systematically altering the accessibility of information, search engines don’t just give us information but shape our collective imagination, and the categories it operates over. (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)

How should the policies of search engines and other information intermediaries be ethically evaluated? It is argued that Kant’s principles for the public use of reason are useful starting points for the formulation of criteria for such an evaluation. The suggestion is, furthermore, that a search engine can be seen to provide a testimony to the user concerning what information that is most relevant to her query. This suggestion is used as the basis for the development of a broadly Kantian (...) account of a rational searcher. It is argued that the search engine companies are morally required to publish their information policies and act in accordance with them but given the threat of search engine spam they can have no obligation to publish the details of their algorithms. (shrink)

I use a theorem from machine learning, called the “No Free Lunch” theorem to support the claim that non-epistemic values are essential to theory choice. I argue that NFL entails that predictive accuracy is insufficient to favor a given theory over others, and that NFL challenges our ability to give a purely epistemic justification for using other traditional epistemic virtues in theory choice. In addition, I argue that the natural way to overcome NFL’s challenge is to use non-epistemic values. If (...) my argument holds, non-epistemic values are entangled in theory choice regardless of human limitations and regardless of the subject matter. Thereby, my argument overcomes objections to the main lines of argument revealing the role of values in theory choice. At the end of the paper, I argue that, contrary to common conception, the epistemic challenge arising from NFL is distinct from Hume’s problem of induction and other forms of underdetermination. (shrink)

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

Based on the 1972 Radcliffe Lectures, this book presents a systematic treatment of the conceptual framework as well as the practical problems of measurement of inequality.