Clinton Castro

The ethical matrix is a tool originally developed by Ben Mepham to, “help decision-makers … reach sound judgements or decisions about the ethical acceptability and/or optimal regulatory controls for existing or prospective technologies” (Mepham et al. 2006, Ethical Matrix Manual, p. 5). The idea behind the matrix is simple but powerful: decision-makers can improve their ethical thinking by identifying relevant affected parties and systematically reflecting on how the technology in question affects these parties in terms of three foundational values: wellbeing, autonomy, and fairness. As Cathy O’Neil and Hanna Gunn observe, the ethical matrix can be profitably tailored for ethical reflection about near-term AI, “algorithms that are already in place in a variety of public and private sectors” (O’Neil and Gunn, 2020, “Near-Term Artificial Intelligence and the Ethical Matrix”, p. 237). O’Neil and Gunn’s tailored ethical matrix replaces Mepham’s list of values with “efficiency,” “false positives,” “false negatives,” “transparency,” “predictive parity,” and “consistency in data quality.” Theirs is, indeed, a helpful tool for reflecting on the ethics of near-term AI. However, it suffers from at least three shortcomings: the categories they use for reflection are not values (ethical or otherwise); their list of concerns is both unwieldy and incomplete; their matrix (as well as Meacham’s) is missing tools for incorporating context into our evaluations of technology. In this paper, I develop a new ethical matrix, the bi-level ethical matrix for near-term AI, that addresses these concerns.

In this chapter, we offer a review of the empirical findings that animate our concerns about the effects of mobile devices on individuals (the troublesome findings about the effects of mobile devices on collectives is reviewed in Chap. 7). We begin by clarifying what we mean by mobile devices, noting that these devices are not our only concern. We also explain why they are our primary concern. In short, we are especially concerned by mobile devices because their mobility, combined with their ability to track our behavior and provide real-time feedback to it, make them powerful behavior modification devices. After explaining how mobile devices can modify our behavior and why—given the structure of the attention economy—the interests of the developers of these technologies are not always in alignment with our own, we show that there is reason to think that mobile devices have detrimental effects on all eight facets of autonomy outlined in the preceding chapter.

Here we offer some concluding remarks and take stock of the arguments made throughout the book. We revisit the three examples from the first chapter, using the conclusions of previous chapters to explain what is going wrong in those scenarios. Each case demonstrates different ways that the attention economy threatens to undermine autonomy, and they underscore the importance of the moral obligations we defended in previous chapters. We conclude by offering some reasons to be optimistic about this situation and to hope that the situation will begin to improve once we start taking steps (both collectively and individually) to address these issues.

In the 1950s, duties to the self became unpopular in mainstream moral philosophy. We explain why some ethicists became skeptical of them, and we present Kant’s position on duties to oneself. Kantian moral philosophers have long maintained the existence of duties to oneself. After presenting this historical backdrop, we then provide our definition of digital minimalism; we understand this as a virtue—a robust disposition to do what is morally required. Given the conclusions of the last two chapters (viz. that autonomy matters morally and that mobile devices undermine autonomy), we argue that you have a moral duty to protect your autonomy from this threat. We define digital minimalism as the disposition to use mobile devices for a reasonable amount of time in light of your chosen ends. We conclude by showing how this duty fits in within the broader taxonomy of Kant’s ethical duties. In our view, the duty to be a digital minimalist is an imperfect duty because it is a duty to promote an obligatory end (one’s own perfection).

In this chapter, we introduce the duty to be an attention ecologist, one who promotes digital minimalism in others. After arguing for the existence of this duty on Kantian grounds (i.e., as following from a duty to respect humanity), we address an objection from Kant himself, who thought that we cannot be obliged to perfect others (which attention ecology seems to demand). We rebut the objection and explore one (of two) sides of attention ecology, i.e., the duty understood as a duty of virtue (in contrast to a duty of right, which we explore in Chap. 6). Here, “virtue” and “right” are Kantian terms of art, the core difference being that duties of virtue cannot be coercively enforced, whereas duties of right can. We show that attention ecology, understood as a duty of virtue, has implications for parents, teachers, friends, employers, and developers. The demands of the duty are conditioned by relationships that constitute these roles; the demands it makes of parents, for instance, are different from those it makes of employers because the relationship between parents and children is different from that of employers and workers. Much of the chapter is dedicated to exploring these differences.

Philosophers have defined autonomy in a variety of ways. In this chapter, we present a Kantian account of personal autonomy as the capacity to set and pursue your own ends (which Kant refers to as “humanity”). We explain how this involves two distinct components: capacity and authenticity. To have autonomous capacities, you must possess baseline abilities (to form intentions, evaluate commitments, etc.); be free of external constraints; be free of certain cognitive inhibitions, and you must have a sufficiently wide range of options. In order for your desires to count as authentic, they must be free of manipulation and coercion, consistent with other motivational states, free from alienation, and they must not be adaptive preferences. After developing our conception of autonomy, we explain and defend Kant’s view that we are morally obligated to respect humanity in ourselves and in others. We conclude by briefly explaining why other moral theories have similar commitments to the moral significance of autonomy.

In this chapter, we summarize the main arguments of the book. Our central thesis is that the moral weight of autonomy gives us reasons to restructure our relationship with mobile devices and the attention economy. In Chap. 2, we explain what autonomy is and why it matters morally. In Chap. 3, we present empirical evidence to justify our concern with mobile devices. We then turn, in Chap. 4, to Kantian moral arguments about the duty we owe to ourselves to protect and safeguard our capacities. We extend this argument in Chap. 5 by discussing duties we owe to others (especially parents to children, teachers to students, employers to employees, and developers to users). In Chap. 6, we explore the possibility of using government regulation to rein in the attention economy, and we justify these arguments by means of Kant’s political philosophy. Having exhausted our concerns with individual-level autonomy, we discuss group-level autonomy in Chap. 7, and we argue that the attention economy contributes to things like polarization which put a drag on democratic legitimacy. We conclude in Chap. 8 by taking stock of our arguments and offering some reasons for optimism.

This chapter begins with a discussion of the distinction between duties of virtue (which cannot be coercively enforced) and duties of right (which can). It explores attention ecology (the duty to promote digital minimalism in others), understood as a duty of right. This takes us into the territory of Kantian political philosophy. The core tenets of Kant’s political philosophy are introduced and applied to the question of regulating the attention economy through legislation. One core tenant is that state interference can be legitimately called upon to protect freedom. Understanding certain harms of the attention economy (e.g., damaging a child’s capacities for setting and pursuing her ends) as threats to freedom opens the door to revisiting some previously discussed cases (e.g., education, employment, and the development of applications) from the point of view of legislation. It is important to note that while regulating the attention economy via legislation is explored, we do not advocate this as the only means by which the attention economy should be checked. In this chapter and others, we emphasize the important role that culture and education can and should play in restructuring our relationship with our devices.

The demands of fair machine learning are often expressed in probabilistic terms. Yet, most of the systems of concern are deterministic in the sense that whether a given subject will receive a given score on the basis of their traits is, for all intents and purposes, either zero or one. What, then, can justify this probabilistic talk? We argue that the statistical reference classes used in fairness measures can be understood as defining the probability that hypothetical persons, who are representative of social roles, will receive certain goods. We call these hypothetical persons “representative individuals.” We claim that what we owe to actual, concrete individuals—whose individual chances of receiving the good in the system might be extreme (i.e., either zero or one)—is that their representative individual has an appropriate probability of receiving the good in question. While less immediately intuitive than other approaches, we argue that the representative individual approach has important advantages over other ways of making sense of this probabilistic talk in the context of fair machine learning.

Recently, there has been much discussion of ‘fair machine learning’: fairness in data‐driven decision‐making systems (which are often, though not always, made with assistance from machine learning systems). Notorious impossibility results show that we cannot have everything we want here. Such problems call for careful thinking about the foundations of fair machine learning. Sune Holm has identified one promising way forward, which involves applying John Broome's theory of fairness to the puzzles of fair machine learning. Unfortunately, his application of Broome's theory appears to be fatally flawed. This article attempts to rescue Holm's central insight – namely, that Broome's theory can be useful to the study of fair machine learning – by giving an alternative application of Broome's theory, which involves thinking about fair machine learning in counterfactual (as opposed to merely statistical) terms.

We argue that students have moral reasons to refrain from using chatbots such as ChatGPT to write certain papers. We begin by showing why many putative reasons to refrain from using chatbots fail to generate compelling arguments against their use in the construction of these papers. Many of these reasons rest on implausible principles, hollowed out conceptions of education, or impoverished accounts of human agency. They also overextend to cases where it is permissible to rely on a machine for something that once required human cognition. We then give our account: you have a moral obligation to respect your own humanity (i.e., your capacity to set and pursue your own ends), and the process of writing a humanities paper is important for the cultivation of your humanity. We conclude by considering objections and offering replies. In the end, we argue that the moral reasons students have to refrain from using chatbots depend crucially on instructors’ ability to make writing assignments worthwhile. This relies on instructors having the right kind of institutional support, which sheds light on implications that this duty has for administrators, legislators, and the general public.

In this open access book, Timothy Aylsworth and Clinton Castro draw on the deep well of Kantian ethics to argue that we have moral duties, both to ourselves and to others, to protect our autonomy from the threat posed by the problematic use of technology. The problematic use of technologies like smartphones threatens our autonomy in a variety of ways, and critics have only begun to appreciate the vast scope of this problem. In the last decade, we have seen a flurry of books making “self-help” arguments about how we could live happier, more fulfilling lives if we were less addicted to our phones. But none of these authors see this issue as one involving a moral duty to protect our autonomy.

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.

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.

Holm (2022) argues that a class of algorithmic fairness measures, that he refers to as the ‘performance parity criteria’, can be understood as applications of John Broome’s Fairness Principle. We argue that the performance parity criteria cannot be read this way. This is because in the relevant context, the Fairness Principle requires the equalization of actual individuals’ individual-level chances of obtaining some good (such as an accurate prediction from a predictive system), but the performance parity criteria do not guarantee any such thing: the measures merely ensure that certain population-level ratios hold.

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of normative egalitarian considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)).

Psychometrics firms such as Cambridge Analytica (CA) and troll factories such as the Internet Research Agency (IRA) have had a significant effect on democratic politics, through narrow targeting of political advertising (CA) and concerted disinformation campaigns on social media (IRA) (U.S. Department of Justice 2019; Select Committee on Intelligence, United States Senate 2019; DiResta et al. 2019). It is natural to think that such activities manipulate individuals and, hence, are wrong. Yet, as some recent cases illustrate, the moral concerns with these activities cannot be reduced simply to the effects they have on individuals. Rather, we will argue, the wrongness of these activities relates to the threats they present to the legitimacy of political orders. This occurs primarily through a mechanism we call “emergent manipulation,” rather than through the sort of manipulation that involves specific individuals.

The attention economy — the market where consumers’ attention is exchanged for goods and services — poses a variety of threats to individuals’ autonomy, which, at minimum, involves the ability to set and pursue ends for oneself. It has been argued that the threat wireless mobile devices pose to autonomy gives rise to a duty to oneself to be a digital minimalist, one whose interactions with digital technologies are intentional such that they do not conflict with their ends. In this paper, we argue that there is a corresponding duty to others to be an attention ecologist, one who promotes digital minimalism in others. Although the moral reasons for being an attention ecologist are similar to those that motivate the duty to oneself, the arguments diverge in important ways. We explore the application of this duty in various domains where we have special obligations to promote autonomy in virtue of the different roles we play in the lives of others, such as parents and teachers. We also discuss the consequences of our arguments for employers, software developers, and policy makers.

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.

The harms associated with wireless mobile devices (e.g. smartphones) are well documented. They have been linked to anxiety, depression, diminished attention span, sleep disturbance, and decreased relationship satisfaction. Perhaps what is most worrying from a moral perspective, however, is the effect these devices can have on our autonomy. In this article, we argue that there is an obligation to foster and safeguard autonomy in ourselves, and we suggest that wireless mobile devices pose a serious threat to our capacity to fulfill this obligation. We defend the existence of an imperfect duty to be a ‘digital minimalist’. That is, we have a moral obligation to be intentional about how and to what extent we use these devices. The empirical findings already justify prudential reasons in favor of digital minimalism, but the moral duty is distinct from and independent of prudential considerations.

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core.

ABSTRACT: So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (chapters 3 and 4), in terms of the conditions required for people to act autonomously (chapters 5 and 6), and in terms of the responsibilities of agents (chapter 7). In this chapter we turn to the ways in which autonomy underwrites democratic governance. Political authority, which is to say the ability of a government to exercise power, may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy. Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We will conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools. The link below is to an open-access copy of the chapter.

ABSTRACT: One important criticism of algorithmic systems is that they lack transparency. Such systems can be opaque because they are complex, protected by patent or trade secret, or deliberately obscure. In the EU, there is a debate about whether the General Data Protection Regulation (GDPR) contains a “right to explanation,” and if so what such a right entails. Our task in this chapter is to address this informational component of algorithmic systems. We argue that information access is integral for respecting autonomy, and transparency policies should be tailored to advance autonomy. To make this argument we distinguish two facets of agency (i.e., capacity to act). The first is practical agency, or the ability to act effectively according to one’s values. The second is what we call cognitive agency, which is the ability to exercise what Pamela Hieronymi calls “evaluative control” (i.e., the ability to control our affective states, such as beliefs, desires, and attitudes). We argue that respecting autonomy requires providing persons sufficient information to exercise evaluative control and properly interpret the world and one’s place in it. We draw this distinction out by considering algorithmic systems used in background checks, and we apply the view to key cases involving risk assessment in criminal justice decisions and K-12 teacher evaluation. The link below is to an open access version of the chapter.

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency.

A growing amount of media is paid for by its consumers through their very consumption of it. Typically, this new media is web-based and paid for by advertising. It includes the services offered by Facebook, Instagram, Snapchat, and YouTube. We offer an ethical assessment of the attention economy, the market where attention is exchanged for new media. We argue that the assessment has ethical implications for how the attention economy should be regulated. To conduct the assessment, we employ two heuristics for evaluating markets. One is the “harm” criterion, which relates to whether the market tends to engender extremely harmful outcomes for individuals or society as a whole. The other is the “agency” criterion, which relates not to the outcomes of the market, but rather, to whether it somehow reflects or has its source in weakened agency. We argue that the attention economy animates concerns with respect to both criteria and that new media should be subject to the same sort of regulation as other harmful, addictive products.

New media (highly interactive digital technology for creating, sharing, and consuming information) affords users a great deal of control over their informational diets. As a result, many users of new media unwittingly encapsulate themselves in epistemic bubbles (epistemic structures, such as highly personalized news feeds, that leave relevant sources of information out (Nguyen forthcoming)). Epistemically paternalistic alterations to new media technologies could be made to pop at least some epistemic bubbles. We examine one such alteration that Facebook has made in an effort to fight fake news and conclude that it is morally permissible. We further argue that many epistemically paternalistic policies can (and should) be a perennial part of the internet information environment.