An issue about the privacy of the clustered groups designed by algorithms arises when attempts are made to access certain pieces of information about those groups that would likely be used to harm them. Therefore, limitations must be imposed regarding accessing such information about clustered groups. In the discourse on group privacy, it is argued that the right to privacy of such groups should be recognised to respect group privacy, protecting clustered groups against discrimination. According to this viewpoint, this right (...) places a duty on others, for example, private companies, institutions, and governments, to refrain from accessing such information. To defend the idea that the right to privacy should be recognised for clustered groups, at least two requirements must be satisfied. First, clustered group privacy must be conceived of as either a collective good or a participatory good. Since these forms of good are of the type from which no member of a group can be excluded from benefiting, the right to them is defined as a group right. Second, there must be group interests on which to base a group right. Group interests can be either the interests of those members that are a result of their being in the group or the interests of the group as a whole that transcend the interests of its members. However, this paper argues that clustered group privacy cannot be conceived of as either a collective or a participatory good because it is possible for some individuals to be excluded from benefiting from it. Furthermore, due to the lack of awareness among individuals that they are members of a clustered group and the nature of a clustered group itself, such groups cannot have the group interests necessary to establish a group right. Hence, the group right to privacy cannot be recognised for these groups, implying that the group right cannot be considered a means to protect clustered groups against discrimination. Instead, this paper suggests that moral principles need to be articulated within an ethics of vulnerability to identify the moral obligations of protecting vulnerable clustered groups. The duty owed to the vulnerable should involve refraining from accessing certain information about clustered groups in specific contexts. This duty is not engendered by the right to privacy of such groups; it is the duty owed to the vulnerable. The findings highlight the need to articulate moral principles regarding privacy and data protection to protect clustered groups in contexts in which accessing information about them could constitute a reason for discriminatory targeting. (shrink)

In We Have Always Been Cyborgs (2021), Stefan L. Sorgner argues that, given the growing economic burden of desirable welfare programs, in order for Western democratic societies to continue to flourish it will be necessary that they establish some form of algocracy (i.e., governance by algorithm). This is argued to be necessary both in order to maintain the sustainability and efficiency of these programs, but also due to the fact that further integration of humans into technical systems provides the only (...) effective means to bridge gaps in functionality and governance. However, Sorgner’s position is entirely insensitive to the design turn in applied ethics, which argues against the neutrality of technology, instead maintaining that technology and society co-construct each other with persistent feedback loops. This, I argue, is a problem for his account inasmuch as technologies, as they become more ubiquitous, likewise become pervasive and inextricable from our sociotechnical infrastructures. As such, less-than-beneficent forces, as current trends illustrate, can appropriate these seemingly banal infrastructures to gear them towards oppressive ends, thereby ultimately threatening the social democracies that Sorgner’s position aims to buttress. (shrink)

In this chapter, I argue that it is impossible to trust the Big Tech companies, in an ethically important sense of trust. The argument is not that these companies are untrustworthy. Rather, I argue that the power to hold the trustee accountable is a necessary component of this sense of trust, and, because these companies are so powerful, they are immune to our attempts, as individuals or nation-states, to hold them to account. It is, therefore, literally impossible to trust Big (...) Tech. After introducing the accounts of trust and power that I deploy, I argue that Big Tech companies have four kinds of power that render them unaccountable: fiscal power, political power, data power, and cognitive power. I conclude by reflecting on recent calls to break up the Big Tech firms, suggesting a new antitrust test in the light of my arguments. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. We also reviewed 12 data ethics (...) frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

Is it wrong to distract? Is it wrong to direct others’ attention in ways they otherwise would not choose? If so, what are the grounds of this wrong – and, in expounding them, do we have to at once condemn large chunks of contemporary digital commerce (also known as the attention economy)? In what follows, I attempt to cast light on these questions. Specifically, I argue – following the pioneering work of Jasper Tran and Anuj Puri – that there is (...) a right to attention, and that its existence underlies some of our claims regarding the wrongness of distractions. However, I depart from both these authors in two respects: first, I present a new way of deriving the right to attention, grounding it in the more fundamental right to mental integrity. Second, I remain agnostic on whether the contemporary business practices of capturing attention in exchange for a variety of digital products and services are plagued by routine violations of the right. (shrink)

This extended abstract summarises some of the basic points of AI ethics and policy as they present themselves now. We explain the notion of AI, the main ethical issues in AI and the main policy aims and means.

While today there is much discussion about the ethics of artificial intelligence, less work has been done on the philosophical nature of AI. Drawing on Bergson and Ricoeur, this paper proposes to use the concepts of time, process, and narrative to conceptualize AI and its normatively relevant impact on human lives and society. Distinguishing between a number of different ways in which AI and time are related, the paper explores what it means to understand AI as narrative, as process, or (...) as the emergent outcome of processes and narratives. It pays particular attention to what it calls the “narrator” and “time machine” roles of AI and the normative implications of these roles. It argues that AI processes and narratives shape our time and link past, present, and future in particular ways that are ethically and politically significant. (shrink)

Research Ethics, Volume 18, Issue 2, Page 151-162, April 2022. In today’s online data-driven world, people constantly shed data and deposit digital footprints. When individuals access health services, governments and health providers collect and store large volumes of health information about people that can later be retrieved, linked and analysed for research purposes. This can lead to new discoveries in medicine and healthcare. In addition, when securely stored and de-identified, the privacy risks are minimal and manageable. In many jurisdictions, ethics (...) committees routinely waive the requirement for researchers to obtain consent from data subjects before using and linking these datasets in an effort to balance respect for individuals with research efficiency. In this paper, I explore the ethical justification for using routinely collected health data for research without consent. I conclude that, not only is this morally justified but also that data subjects have a moral obligation to contribute their data to such research, which would obviate the need for ethics committees to consider consent waivers. In justifying this argument, I look to the duty of easy rescue, distributive justice and draw analogies with vaccination ethics. (shrink)

Is more information always better? Or are there some situations in which more information can make us worse off? Good (1967) argues that expected utility maximizers should always accept more information if the information is cost-free and relevant. But Good's argument presupposes that you are certain you will update by conditionalization. If we relax this assumption and allow agents to be uncertain about updating, these agents can be rationally required to reject free and relevant information. Since there are good reasons (...) to be uncertain about updating, rationality can require you to prefer ignorance. (shrink)

My dissertation is about Bayesian rationality for non-ideal agents. I show how to derive subjective probabilities from preferences using much weaker rationality assumptions than other standard representation theorems. I argue that non-ideal agents might be uncertain about how they will update on new information and consider two consequences of this uncertainty: such agents should sometimes reject free information and make choices which, taken together, yield sure loss. The upshot is that Bayesian rationality for non-ideal agents makes very different normative demands (...) than ideal Bayesian rationality. (shrink)

This chapter examines the possibility of using AI technologies to improve human moral reasoning and decision-making, especially in the context of purchasing and consumer decisions. We characterize such AI technologies as artificial ethics assistants (AEAs). We focus on just one part of the AI-aided moral improvement question: the case of the individual who wants to improve their morality, where what constitutes an improvement is evaluated by the individual’s own values. We distinguish three broad areas in which an individual might think (...) their own moral reasoning and decision-making could be improved: one’s actions, character, or other evaluable attributes fall short of one’s values and moral beliefs; one sometimes misjudges or is uncertain about what the right thing to do is in particular situations, given one’s values; one is uncertain about some fundamental moral questions or recognizes a possibility that some of one’s core moral beliefs and values are mistaken. We sketch why one might think that AI tools could be used to support moral improvement in those areas, and describe two types of assistance: preparatory assistance, including advice and training supplied in advance of moral deliberation; and on-the-spot assistance, including on-the-spot advice and facilitation of moral functioning over the course of moral deliberation. Then, we turn to some of the ethical issues that AEAs might raise, looking in particular at three under-appreciated problems posed by the use of AI for moral self-improvement: namely, reliance on sensitive moral data; the inescapability of outside influences on AEAs; and AEA usage prompting the user to adopt beliefs and make decisions without adequate reasons. (shrink)

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)

Open science (OS) is considered the new paradigm for science and knowledge dissemination. OS fosters cooperative work and new ways of distributing knowledge by promoting effective data sharing (as early and broadly as possible) and a dynamic exchange of research outcomes, not only publications. On the other hand, intellectual property (IP) legislation seeks to balance the moral and economic rights of creators and inventors with the wider interests and needs of society. Managing knowledge outcomes in a new open research and (...) innovation ecosystem is challenging and should become part of the EU’s IP strategy, underpinning EU policies with the new open science–open innovation paradigm. (shrink)

In this article I analyze the academic reception of Jonathan Haidt’s seminal article _The emotional dog and its rational tail: A social intuitionist approach to moral judgment_. My thesis is that in the spheres of philosophy and psychology, this article was initially studied within the neurocentric paradigm, which dominated the field of scientific reflection in the fifteen years following its publication. This neurocentric reading established a specific interpretation of the text with several limitations. However, more recently a digital paradigm has (...) emerged and come to prevail in academia, providing a new perspective from which to return to Haidt’s text. Indeed, this approach makes it possible to unravel elements of the famous article that in the neurocentric paradigm went unnoticed by researchers. Moreover, the digital paradigm manages to better integrate Haidt’s seminal article into his later work as a whole. (shrink)

Artificial intelligence and robotic technologies have become nearly ubiquitous. In some ways, the developments have likely helped us, but in other ways sophisticated technologies set back our interests. Among the latter sort is what has been dubbed the ‘severance problem’—the idea that technologies sever our connection to the world, a connection which is necessary for us to flourish and live meaningful lives. I grant that the severance problem is a threat we should mitigate and I ask: how can we stave (...) it off? In particular, the fact that some technologies exhibit behavior that is unclear to us seems to constitute a kind of severance. Building upon contemporary work on moral responsibility, I argue for a mechanism I refer to as ‘technological answerability’, namely the capacity to recognize human demands for answers and to respond accordingly. By designing select devices—such as robotic assistants and personal AI programs—for increased answerability, we see at least one way of satisfying our demands for answers and thereby retaining our connection to a world increasingly occupied by technology. (shrink)

O artigo explora diferentes sentidos da privacidade e sua relação com o modelo discursivo de democracia radical desenvolvido por Jürgen Habermas em Facticidade e validade (1992), dedicando-se a pensá-la não apenas como componente normativo de uma concepção exigente de Estado democrático de direito, mas também como instrumento voltado à identificação e crítica de patologias democráticas contemporâneas.

This paper argues that calls for neurorights propose an overcomplicated approach. It does this through analysis of ‘rights’ using the influential framework provided by Wesley Hohfeld, whose analytic jurisprudence is still well regarded in its clarificatory approach to discussions of rights. Having disentangled some unclarities in talk about rights, the paper proposes the idea of ‘novel human rights’ is not appropriate for what is deemed worth protecting in terms of mental integrity and cognitive liberty. That is best thought of in (...) terms of Hohfeld’s account of ‘right’ as privilege. It goes on to argue that as privileges, legal protections are not well suited to these cases. As such, they cannot be ‘novel human rights’. Instead, protections for mental integrity and cognitive liberty are best accounted for in terms of familiar and established rational and discursive norms. Mental integrity is best thought of as evaluable in terms of familiar rational norms, and cognitive freedom is constrained by appraisals of sense-making. Concerns about how neurotechnologies might pose particular challenges to mental integrity and cognitive liberty are best protected through careful use of existing legislation on data protection, not novel rights, as it is via data that risks to integrity and liberty are manifested. (shrink)

Simion and Kelp defend a non-anthropocentric account of trustworthy AI, based on the idea that the obligations of AI systems should be sourced in purely functional norms. In this commentary, I highlight some pressing counterexamples to their account, involving AI systems that reliably fulfil their functions but are untrustworthy because those functions are antagonistic to the interests of the trustor. Instead, I outline an alternative account, based on the idea that AI systems should not be considered primarily as tools but (...) as technological participants in social practices. Specifically, I propose to source the obligations of an AI system in the norms that should govern the role it plays within the social practices it participates in, taking into account any changes to the social practices that its participation may bring about. This proposal is anthropocentric insofar as it ascribes obligations to AI systems that are similar to those of human participants in social practices, but only modestly so, as it does not require trustworthy AI to have contentious anthropomorphic capacities (e.g. for consciousness or moral responsibility). (shrink)

If I decide to disclose information about myself, this act can undermine other people’s ability to effectively conceal information about themselves. One case in point involves genetic information: if I share ‘my’ genetic information with others, I thereby also reveal genetic information about my biological relatives. Such dependencies are well-known in the privacy literature and are often referred to as ‘privacy dependencies’. Some take the existence of privacy dependencies to generate a moral duty to sometimes avoid sharing information about oneself. (...) If true, we argue, then it is sometimes justified for others to impose harm on the person sharing the information to prevent them from doing so. This is a highly revisionary implication. Hence, one must either endorse a highly revisionary view on what one may do to protect one’s privacy, or one must reject the view that privacy dependencies can be used to justify a moral duty that constrains choices about sharing information about oneself. (shrink)

People's sense of being subject to digital dataveillance can cause them to restrict their digital communication behavior. Such a chilling effect is essentially a form of self-censorship in everyday digital media use with the attendant risks of undermining individual autonomy and well-being. This article combines the existing theoretical and limited empirical work on surveillance and chilling effects across fields with an analysis of novel data toward a research agenda. The institutional practice of dataveillance—the automated, continuous, and unspecific collection, retention, and (...) analysis of digital traces—affects individual behavior. A mechanism-based causal model based on the theory of planned behavior is proposed for the micro level: An individual's increased sense of dataveillance causes their subjective probability assigned to negative outcomes of digital communication behavior to increase and attitudes toward this communication to become less favorable, ultimately decreasing the intention to engage in it. In aggregate and triggered through successive salience shocks such as data scandals, dataveillance is accordingly hypothesized to lower the baseline of free digital communication in a society through the chilling effects mechanism. From the developed theoretical model, a set of methodological consequences and questions for future studies are derived. (shrink)

How do social norms influence our choices? And does the presence of biased norms affect what we owe to each other? Looking at empirical research relating to PrEP rollout in HIV prevention policy, a...

Co-authored by a Computer Scientist and a Digital Humanist, this article examines the challenges faced by cultural heritage institutions in the digital age, which have led to the closure of the vast majority of born-digital archival collections. It focuses particularly on cultural organizations such as libraries, museums and archives, used by historians, literary scholars and other Humanities scholars. Most born-digital records held by cultural organizations are inaccessible due to privacy, copyright, commercial and technical issues. Even when born-digital data are publicly (...) available, users often need to physically travel to repositories such as the British Library or the Bibliothèque Nationale de France to consult web pages. Provided with enough sample data from which to learn and train their models, AI, and more specifically machine learning algorithms, offer the opportunity to improve and ease the access to digital archives by learning to perform complex human tasks. These vary from providing intelligent support for searching the archives to automate tedious and time-consuming tasks. In this article, we focus on sensitivity review as a practical solution to unlock digital archives that would allow archival institutions to make non-sensitive information available. This promise to make archives more accessible does not come free of warnings for potential pitfalls and risks: inherent errors, "black box" approaches that make the algorithm inscrutable, and risks related to bias, fake, or partial information. Our central argument is that AI can deliver its promise to make digital archival collections more accessible, but it also creates new challenges - particularly in terms of ethics. In the conclusion, we insist on the importance of fairness, accountability and transparency in the process of making digital archives more accessible. (shrink)

This paper analyzes the ethics of social science research employing big data. We begin by highlighting the research gap found on the intersection between big data ethics, SSR and research ethics. We then discuss three aspects of big data SSR which make it warrant special attention from a research ethics angle: the interpretative character of both SSR and big data, complexities of anticipating and managing risks in publication and reuse of big data SSR, and the paucity of regulatory oversight and (...) ethical recommendations on protecting individual subjects as well as societies when conducting big data SSR. Against this backdrop, we propose using David Resnik’s research ethics framework to analyze some of the most pressing ethical issues of big data SSR. Focusing on the principles of honesty, carefulness, openness, efficiency, respect for subjects, and social responsibility, we discuss three clusters of ethical issues: those related to methodological biases and personal prejudices, those connected to risks arising from data availability and reuse, and those leading to individual and social harms. Finally, we advance considerations to observe in developing future ethical guidelines about big data SSR. (shrink)

The paper presents an ethical analysis and constructive critique of the current practice of AI ethics. It identifies conceptual substantive and procedural challenges and it outlines strategies to address them. The strategies include countering the hype and understanding AI as ubiquitous infrastructure including neglected issues of ethics and justice such as structural background injustices into the scope of AI ethics and making the procedures and fora of AI ethics more inclusive and better informed with regard to philosophical ethics. These measures (...) integrate the perspective of AI justice into AI ethics, strengthening its capacity to provide comprehensive normative orientation and guidance for the development and use of AI that actually improves human lives and living together. (shrink)

All societies have to balance privacy claims with other moral concerns. However, while some concern for privacy appears to be a common feature of social life, the definition, extent and moral justifications for privacy differ widely. Are there better and worse ways of conceptualising, justifying, and managing privacy? These are the questions that lie in the background of this thesis. -/- My particular concern is with the ethical issues around privacy that are tied to the rise of new information and (...) communication technologies (henceforth “ICT”). The focus here is on technologies involved in processing personal data in its broadest sense, including “any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction.” The issue of moral concern is commonly designated as that of 'data privacy'. -/- The first step is to consider why privacy matters, or ought to matter, in the first place. And here we run into our first difficulty, since both the conceptualisation of privacy and the justification for the right to privacy are matters for contention. On the one hand, we are animated by a moral concern for privacy in some sense; on the other, we lack the conceptual and moral resources for making sense of this concern or for promoting particular social policies and criticising others. This often-cited dichotomy and its ensuing confusion have been the central motivation for most of the philosophical literature on privacy – including this thesis. -/- In what follows, I will suggest a way out of the confusion that pervades the current debate on data privacy. I will essentially argue two claims: one, that the concept of (data) privacy is uniquely characterised by a particular context within which we exchange information; two, that because (data) privacy constitutes an intrinsic value, its moral concerns and justifications ought to be deliberated as such. -/- The first chapter sketches the current data privacy landscape by introducing a number of concepts and arguing the unique nature of the landscape. I argue that the traditional distinction between four kinds of privacy (physical, mental, decisional and informational) is no longer accurate, given the technological context that reduces, unifies and processes all aspects of people’s lives by means of digital data. In fact, to hold on to the distinction leads to a fundamental misconception of data privacy. I further argue that today’s distinction between data protection and data privacy runs the risk of ignoring the fact that the former is an inherent aspect of the latter. These arguments allow me to settle on a (provisional) definition of data privacy: controlling how data about identifiable people is processed by ICT. -/- Next, I draw a line between descriptive and normative accounts of data privacy, and explain why this book belongs to the latter category. After a brief discussion of the coherence and distinctiveness theses, and of the difference between instrumental and intrinsic theories of privacy, I introduce the problem of data privacy as a distinct, coherent, and intrinsic moral concern. Three arguments support this position: one, data privacy is a coherent moral concern because it is motivated by a singular moral concept; two, data privacy is a distinct moral concern because the privacy challenges we face today are of a fundamentally new nature – distinct from any moral concerns we’ve faced in the past; and three, data privacy is an intrinsic moral concern that is predominantly justified, not by what it instrumentally protects us from, but by what it intrinsically constitutes. -/- Chapter Two makes the case against a harm-based, consequentialist approach to data privacy. After a brief introduction to the basic tenets of consequentialism I discuss harm-based arguments in favour as well as against privacy. I explain how privacy may protect us against psychological distress, but at the same time constitute the source of that distress; how privacy may be necessary for the proper functioning of democratic societies, but at the same time provide a cloak for anti-social and immoral behaviour; and how privacy protects us against the improper use of our personal data by others, but at the same enables us to conceal information or spread false information about ourselves in ways that are harmful to others. Next, I argue that consequentialism essentially relies on two flawed presumptions – that we know what harm is, and that we know how to predict it – and discuss the controversies surrounding definitions and predictions of harm. This argument becomes especially pertinent in today’s rapidly changing technological context: we may guess, and make assumptions, perhaps even find correlations, but don’t really know or understand (yet) with any degree of certainty the harmful consequences today’s data privacy violations are causing or will eventually cause – if any. -/- I illustrate this with examples of recent data privacy violations, and with a brief overview of consequentialist arguments for and against data privacy. Those who invoke consequentialist arguments against data privacy claim that personal data is nothing more than a raw material that can be turned into something useful and valuable without harming anyone. Choosing to move fast and break things – a popular mantra that promotes the benefits of technological “permissionless innovation” over and above its risks and costs – they draw our attention to the many benefits free data flows generate. Those who rely on consequentialist arguments in favour of data privacy, on the other hand, insist on actual and potential misuse of personal data. Rather careful than sorry, they draw our attention to the urgent need to take back control and protect ourselves against data privacy violations – precisely because we don’t know which harm they may cause in the future. Others go one step further and argue that these violations are part of a broader ideology of power. -/- What Chapter Two demonstrates is that vague definitions, unreliable predictions and inconclusive allegations of harm from both sides of the debate perpetuate a stalemate that fails to produce morally defensible answers to our growing data privacy concerns. Deliberating about data privacy from a consequentialist perspective is a dead end. -/- Chapter Three proposes an alternative to the harm-based approach, namely that privacy is an intrinsic value that deserves moral protection for its own sake. After a brief account of what I take values to be, and why we have a need to protect them, I introduce the foundational value of respect for persons, and lay out my main argument in two steps. The conceptual component of my argument returns to the definition of data privacy I had temporarily settled on in Chapter One, but now with the added consideration of the particular context, and particular circumstances and attitudes, within which information is exchanged. This addition is crucial, as it reveals the fundamental value that is at stake in our data privacy concerns, namely the inherent value of each and every person, which we protect by upholding an ethical principle commonly known as the principle of respect for persons. It is for this reason that the violation of privacy cannot (only) be expressed in terms of harm. I further argue that the principle of respect for persons is consistent with, and in fact unthinkable without, some degree of privacy. -/- I reply to potential challenges to a value-based approach to data privacy by showing that (i) values and value trade-offs are fundamentally different from consequential benefits and cost-benefit analyses; (ii) the suspension of one value in favour of another is not inherently contradictory; what matters is morally defensible deliberation about values and valid individual consent; (iii) data privacy concerns cannot be reduced to other moral concerns and therefore deserve a specific, distinct protection. -/- Finally, because this is a thesis in applied ethics, I attempt an answer to the question as to how we ought to apply a value-based approach to practical data privacy concerns. I argue that a revised, three-pronged version of John Rawls’ reflective equilibrium, founded on the non-negotiable principle of respect for persons, is the best possible procedure to deliberate data privacy concerns in relation to other moral values. (shrink)