I introduce a new distinction between two types of paternalism, which I call ‘internal’ and ‘external’ paternalism. The distinction pertains to the question of whether the paternalized subject’s current evaluative judgments are mistaken relative to a standard of correctness that is internal to her evaluative point of view—which includes her ‘true’ or ‘ideal’ self—as opposed to one that is wholly external. I argue that this distinction has important implications for (a) the distinction between weak and strong paternalism; (b) the distinction between soft and hard paternalism; and (c) medical practice, where internal paternalism can, and probably often does, occur.

The conception of responsibility for beliefs typically assumed in the literature mirrors the practices of accountability for actions. In this paper, I argue that this trend leaves a part of what it is to be responsible unduly neglected, namely the practices of attributability. After offering a diagnosis for this neglect, I bring these practices into focus and develop a virtue-theoretic framework to vindicate them. I then investigate the specificity of the belief case and conclude by resisting two challenges, namely that attributability cannot amount to genuine responsibility and that it can be reduced to a sort of accountability.

This paper explores a neglected normative dimension of algorithmic opacity in the workplace and the labor market. It argues that explanations of algorithms and algorithmic decisions are of noninstrumental value. That is because explanations of the structure and function of parts of the social world form the basis for reflective clarification of our practical orientation toward the institutions that play a central role in our life. Using this account of the noninstrumental value of explanations, the paper diagnoses distinctive normative defects in the workplace and economic institutions which a reliance on AI can encourage, and which lead to alienation.

More than a decade of exacting scientific research involving paleontological fragments and ancient DNA has lately produced a series of pronouncements about a purportedly novel population of archaic hominins dubbed “the Denisova.” The science involved in these matters is both technically stunning and, socially, at times a bit reckless. Here I discuss the responsibilities which scientists incur when they make inductively risky pronouncements about the different relative contributions by Denisovans to genomes of members of apparent subpopulations of current humans (i.e., the so-called “races”). This science is sensational: it is science which empirically speculates, to the public delight’s and entertainment, about scintillating topics such as when humans evolved, where we came from, and who else we were having sex with during our early hominin history. An initial characterization of sensational science emerges from my discussion of the case, as well as a diagnosis of an interactive phenomenon termed amplified inductive risk.

It is natural to think that social groups are concrete material particulars, but this view faces an important objection. Suppose the chess club and nature club have the same members. Intuitively, these are different clubs even though they have a common material basis. Some philosophers take these intuitions to show that the materialist view must be abandoned. I propose an alternative explanation. Social groups are concrete material particulars, but there is a psychological explanation of nonidentity intuitions. Social groups appear coincident but nonidentical because they are perceived to be governed by conflicting social norms.

Many philosophers now see meaning in life as a key evaluative category that stands alongside well-being and moral goodness. Our lives are assessed not only by how well they go for us and how morally good they are, but also by their meaningfulness. In this article, I raise a challenge to this view. Theories of meaning in life closely resemble theories of well-being, and there is a suspicion that the former collapse into the latter. I develop this challenge showing that it is formidable. I then answer it by offering a novel account of what meaning in life is and how it differs from well-being. The account I offer is able to resist the strongest form of the challenge while also having much intuitive appeal.

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 (ML) 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.

Social networking services provide services in return for rights to commercialize users’ personal data. We argue that what makes this transaction permissible is not users’ autonomous consent but the provision of sufficiently valuable opportunities to exchange data for services. We argue that the value of these opportunities should be assessed for both (a) a range of users with different decision-making abilities and (b) third parties. We conclude that regulation should shift from aiming to ensure autonomous consent towards ensuring that users face options that they will use to advance individual and common interests.

Both scientists and philosophers of science have recently emphasized the importance of promoting transparency in science. For scientists, transparency is a way to promote reproducibility, progress, and trust in research. For philosophers of science, transparency can help address the value-ladenness of scientific research in a responsible way. Nevertheless, the concept of transparency is a complex one. Scientists can be transparent about many different things, for many different reasons, on behalf of many different stakeholders. This paper proposes a taxonomy that clarifies the major dimensions along which approaches to transparency can vary. By doing so, it provides several insights that philosophers and other science studies scholars can pursue. In particular, it helps address common objections to pursuing transparency in science, it clarifies major forms of transparency, and it suggests avenues for further research on this topic.

Algorithmic communications pose several challenges to democracy. The three phenomena of filtering, hypernudging, and microtargeting can have the effect of polarizing an electorate and thus undermine the deliberative potential of a democratic society. Algorithms can spread fake news throughout the society, undermining the epistemic potential that broad participation in democracy is meant to offer. They can pose a threat to political equality in that some people may have the means to make use of algorithmic communications and the sophistication to be immune from attempts at manipulation, while other people are vulnerable to manipulation by those who use these means. My concern here is with the danger that algorithmic communications can pose to political equality, which arises because most citizens must make decisions about what and who to support in democratic politics with only a sparse budget of time, money, and energy. Algorithmic communications such as hypernudging and microtargeting can be a threat to democratic participation when persons are operating in environments that do not conduce to political sophistication. This constitutes a deepening of political inequality. The political sophistication necessary to counter this vulnerability is rooted for many in economic life and it can and ought to be enhanced by changing the terms of economic life.

Recent rival attempts in the philosophy of science to put forward a general theory of the properties that all (and only) natural kinds across the sciences possess may have proven to be futile. Instead, I develop a general methodological framework for how to philosophically study kinds. Any kind has to be investigated and articulated together with the human aims that motivate referring to this kind, where different kinds in the same scientific domain can answer to different concrete aims. My core contention is that nonepistemic aims, including environmental, ethical, and political aims, matter as well. This is defended and illustrated based on several examples of kinds, with particular attention to the role of social-political aims: species, race, gender, as well as personality disorders and oppositional defiant disorder as psychiatric kinds. Such nonepistemic aims and values need not always be those personally favoured by scientists but may have to reflect values that matter to relevant societal stakeholders. Despite the general agenda to study “kinds,” I argue that philosophers should stop using the term “natural kinds,” as this label obscures the relevance of human interests and the way in which many kinds are based on contingent social processes subject to human responsibility.

Automated Influence is the use of Artificial Intelligence (AI) to collect, integrate, and analyse people’s data in order to deliver targeted interventions that shape their behaviour. We consider three central objections against Automated Influence, focusing on privacy, exploitation, and manipulation, showing in each case how a structural version of that objection has more purchase than its interactional counterpart. By rejecting the interactional focus of “AI Ethics” in favour of a more structural, political philosophy of AI, we show that the real problem with Automated Influence is the crisis of legitimacy that it precipitates.