Are we being manipulated online? If so, is being manipulated by online technologies and algorithmic systems notably different from human forms of manipulation? And what is under threat exactly when people are manipulated online? This volume provides philosophical and conceptual depth to debates in digital ethics about online manipulation. The contributions explore the ramifications of our increasingly consequential interactions with online technologies such as online recommender systems, social media, user-friendly design, micro-targeting, default-settings, gamification, and real-time profiling. The authors in this (...) volume address four broad and interconnected themes: • What is the conceptual nature of online manipulation? And how, methodologically, should the concept be defined? • Does online manipulation threaten autonomy, freedom, and meaning in life, and if so, how? • What are the epistemic, affective, and political harms and risks associated with online manipulation? • What are legal and regulatory perspectives on online manipulation? The Philosophy of Online Manipulation brings these various considerations together to offer philosophically robust answers to critical questions concerning our online interactions with one another and with autonomous systems. It will be of interest to researchers and advanced students working in moral philosophy, digital ethics, philosophy of technology, and the ethics of manipulation. (shrink)

In this chapter, we turn our attention to the effects of the attention economy on our ability to act autonomously as a group. We begin by clarifying which sorts of groups we are concerned with, which are structured groups (groups sufficiently organized that it makes sense to attribute agency to the group itself). Drawing on recent work by Purves and Davis (2022), we describe the essential roles of trust (i.e., depending on groups to fulfill their commitments) and trustworthiness (i.e., the (...) property of a group that makes trusting them fitting) in autonomous group action, with particular emphasis on democratic institutions (which we view as group agents) and democratic legitimacy (which depends on trust and trustworthiness). We then explain how engagement maximization promotes polarization, which is detrimental to trust and trustworthiness and, in turn, democratic legitimacy and democratic institutions. We close by considering what groups might do to protect themselves from the threat posed to them by the attention economy. (shrink)

A discussion concerning whether to conceive Artificial Intelligence systems as responsible moral entities, also known as “artificial moral agents”, has been going on for some time. In this regard, we argue that the notion of “moral agency” is to be attributed only to humans based on their autonomy and sentience, which AI systems lack. We analyze human responsibility in the presence of AI systems in terms of meaningful control and due diligence and argue against fully automated systems in medicine. With (...) this perspective in mind, we focus on the use of AI-based diagnostic systems and shed light on the complex networks of persons, organizations and artifacts that come to be when AI systems are designed, developed, and used in medicine. We then discuss relational criteria of judgment in support of the attribution of responsibility to humans when adverse events are caused or induced by errors in AI systems. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...) who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to Kleinberg (...) et al. (2016), Chouldechova (2017), and Corbett-Davies et al. (2017), which show that classification parity and calibration are often incompatible. This paper aims to argue that classification parity, calibration, and a newer, interesting measure called counterfactual fairness are unsatisfactory measures of fairness, offer a general diagnosis of the failure of these measures, and sketch an alternative approach to understanding fairness in machine learning. (shrink)

This paper examines the practice of using predictive systems to lengthen the prison sentences of convicted persons when the systems forecast a higher likelihood of re-offense or re-arrest. There has been much critical discussion of technologies used for sentencing, including questions of bias and opacity. However, there hasn’t been a discussion of whether this use of predictive systems makes sense in the first place. We argue that it does not by showing that there is no plausible theory of punishment that (...) supports it. (shrink)

The spread of misinformation and fake news raises important problems for our society and for our democracy. From the January 6 attack on the U.S. Capitol to vaccine hesitancy, from suppressing voter turnout to peddling conspiracy theories, we know that these problems are real and need to be taken seriously. While misinformation is not a new problem for democracy, it can spread more quickly and easily because of new media’s design and popularity. Given these problems, it is encouraging that some (...) technology companies are taking steps to reduce the spread of misinformation and fake news on the platforms they manage. Despite this seemingly positive development, some scholars have criticized some interventions designed to combat the spread of misinformation and fake news as paternalistic. For example, a 2019 Facebook intervention called Click-Gap aimed to reduce the amount of low-quality content (including fake news and misinformation) that users see on their NewsFeed. Click-Gap has been criticized as an instance of epistemic paternalism because it was adopted (1) with the goal of improving the epistemic status of its users and (2) irrespective of what the company believed the wishes of its users to be. If interventions like Click-Gap are problematic because paternalistic, those of us interested in the ethics of technology would face a dilemma—either endorse technology companies treating their users paternalistically or endorse their failing to act to combat the spread of misinformation and fake news on their platforms. Both options seem to me to be problematic. While paternalism may sometimes be permissible, I think we should be very hesitant to endorse a paternalistic relationship between technology companies and their users. The relationship does not seem to bear the right sort of structure to one in which paternalism might be appropriate, if it ever is. The second option seems, if anything worse: surely technology companies should not stand by and change nothing about their platforms despite the spread of misinformation and fake news in those spaces. In this paper, I argue that Click-Gap and interventions like it are not paternalistic, contrary to the conclusion of other scholars. Further, I will argue that the focus on paternalism itself is actually a red herring here. While not just any intervention or strategy that purports to reduce fake news and misinformation is permissible, we should want technology companies to take user well-being seriously and be able to take that well-being as a direct reason for action. Their doing so is not paternalistic nor even morally problematic, and it should not be criticized as such. (shrink)