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.

Is there something bad about being friends with seriously bad people? Intuitively, it seems so, but it is hard to see why this should be. This is especially the case since some other kinds of loving relationship with bad people look morally acceptable or even good. In this paper, I argue that friendship inherently involves taking one’s friends seriously, which involves openness to their beliefs, concerns, and subjective interests. Deeply immoral views and attitudes ought not to be taken seriously or considered as options, and I argue that this explains why being friends with bad people is itself morally problematic. I go on to contrast this with Jessica Isserow’s (2018) explanation of what’s bad about friendship with bad people, and I suggest that my account is better placed to explain why friendships with bad people are morally problematic but some other loving relationships with bad people are not.

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.

Several science studies scholars report instances of scientific “hype,” or sensationalized exaggeration, in journal articles, institutional press releases, and science journalism in a variety of fields (e.g., Caulfield and Condit 2012). Yet, how “hype” is being conceived varies. I will argue that hype is best understood as a particular kind of exaggeration, one that explicitly or implicitly exaggerates various positive aspects of science in ways that undermine the goals of science communication in a particular context. This account also makes clear the ways that value judgments play a role in judgments of “hype,” which has implications for detecting and addressing this problem.

In the last two decades, a robust consensus has emerged among philosophers of science, whereby political, ethical, or social values must play some role in scientific inquiry, and that the ‘value-free ideal’ is thus a misguided conception of science. However, the question of how to distinguish, in a principled way, which values may legitimately influence science remains. This question, which has been dubbed the ‘new demarcation problem,’ has until recently received comparatively less attention from philosophers of science. In this paper, I appeal to Rawls’s theory of justice (1971) on the basis of which I defend a Rawlsian solution to the new demarcation problem. As I argue, the Rawlsian solution places plausible constraints on which values ought to influence scientific inquiry, and, moreover, can be fruitfully applied to concrete cases to determine how the conflicting interests of stakeholders should be balanced. After considering and responding to the objection that Rawls’s theory of justice applies only to the “basic structure” of society, I compare the Rawlsian solution to some other approaches to the new demarcation problem, especially those that emphasize democratic criteria.

We are increasingly subject to the power of technological systems relying on big data and AI. These systems are reshaping the welfare state and the administration of criminal justice. They are used to police tax evasion, track down child abusers, and model the spread of the pandemic. And they are used to weaponize vast surveillance networks through facial recognition technology. But algorithmic power extends far beyond the state: we spend ever more time working, socialising, and consuming within digital platforms. Our experiences are governed by algorithms that are constantly monitoring and shaping our behaviour and our attention, automatically selecting what we do and do not see. These online experiences have offline consequences, among them an unprecedented challenge to democratic processes worldwide.

How does trust relate to faith? We do not know of a theory-neutral way to answer our question. So, we begin with what we regard as a plausible theory of faith according to which, in slogan form, faith is resilient reliance. Next, we turn to contemporary theories of trust. They are not of one voice. Still, we can use them to indicate ways in which trust and faith might both differ from and resemble each other. This is what we do. Along the way, we evaluate substantive issues related to these possible differences and similarities.

Some people of colour feel shame in response to racist incidents. This phenomenon seems puzzling since, plausibly, they have nothing to feel shame about. This puzzle arises because we assume that targets of racism feel shame about their race. However, I propose that when an individual is racialised as non-White in a racist incident, shame is sometimes prompted, not by a negative self-assessment of her race, but by her inability to choose when her stigmatised race is made salient. I argue that this can make sense of some shame responses to racism. My account also helps to highlight some of the emotional and cognitive costs of racism that have their root in shame as well as a new form of hermeneutical injustice and distinctive communicative harms, contributing to a fuller picture of what is objectionable about racism.

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.

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.