Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or (...) treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (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)

Digital platforms allow wildlife enthusiasts to share information with larger audiences than ever before. However, by heightening awareness and human interaction, they can threaten the well-being of non-human animals (henceforth “animals”). One example of this is increased stress to Snowy Owls (Bubo scandiacus) from close observation by large numbers of people made possible by accurate, timely, and widely-distributed location information. In this paper, we examine the ethics of animal privacy, wildlife observation, and information sharing on digital platforms, using Snowy Owls (...) as a case study. We begin by reviewing Snowy Owl migration and population statistics, trends in information sharing about and observation of Snowy Owls, and resulting harms to Snowy Owls. We then argue that animals have morally weighty privacy interests that ground human obligations to protect their privacy. These obligations are strengthened by additional concerns about species protection and ecological conservation. Our arguments support an array of privacy protections, including obligations on the part of wildlife observers, researchers, and other groups. However, because the catalyst for this paper is novel information technologies, we make a focused set of recommendations applicable to digital platforms. Specifically, we argue that platforms ought to restrict public information sharing when disclosure is likely to endanger individual animals, species, or ecosystems. (shrink)

AI-enabled predictive analytics is widely deployed in clinical care settings for healthcare monitoring, diagnostics and risk management. The technology may offer valuable insights into individual and population health patterns, trends and outcomes. Predictive analytics may, however, also tangibly affect individual patient privacy and the right thereto. On the one hand, predictive analytics may undermine a patient’s state of privacy by constructing or modifying their health identity independent of the patient themselves. On the other hand, the use of predictive analytics may (...) violate the patient’s right to privacy if the patient has no control over the use or output of the technology. These repercussions ultimately erode patient autonomy and agency. This paper discusses these implications in further detail, and proposes possible measures for their mitigation. They involve the incorporation in the AI systems of accuracy-enhancing statistical models and methods, more privacy-conscious institutional policies and practices, and effective choice for patients to accept or refuse diagnostics and treatment drawing on AI-enabled predictive analytics. (shrink)

The essay proposes to use alienation as a heuristic and conceptual tool for the analysis of the impact of digital information and communication technologies (ICTs) on users. It follows a historical materialist understanding, according to which data can be considered as things produced in an industrial fashion. A representational interpretation, according to which data would merely reflect a given reality, is untenable. It will be argued instead to understand data as an additional layer which has a transformative impact on reality (...) as a whole. The analysis follows the early Marx in thinking of alienation as a structural condition which holds even if it is not experienced psychologically by users. Alienation means that the desire to express oneself and achieve social recognition, self-realization, and participation through the use of ICTs is incongruent to the inherent purposes and functions of these technologies. Digital platforms create the ideological illusion that users contribute to the production of data solely for their own gratification. Users, in turn, react to structural alienation either by internalizing it, in which case they may become enthusiastic data producers, or by imposing alienating conditions on other users. Finally, it will be shown that a strong notion of privacy is less suited to articulate concerns about the impact of ICTs because it expresses a desire for privilege and individual control which has become unfulfillable in the environment of data industries. The essay closes by showing some of the changes in institutional structures and behavior that could follow from the focus on alienation. (shrink)

Purpose Privacy has been understood as about one’s own information, information that is not one’s own is not typically considered with regards to an individual’s privacy. This paper aims to draw attention to this issue for conceptualizing privacy when one’s privacy is breached by others’ information. Design/methodology/approach To illustrate the issue that others' information can breach one's own privacy, this paper uses real-world applications of forensic genealogy and recommender systems to motivate the discussion. Findings In both forensic genealogy and recommender (...) systems, the individual’s privacy is breached by information that is not one’s own. The information that breached one’s privacy, by its nature, is beyond the scope of an individual, which is a phenomenon that has already been captured by emerging discussions about group privacy. This paper further argues that the underlying issue reflected by the examples of forensic genealogy is an extreme case even under the consideration of group privacy. This is because, unlike recommender systems that rely on large amounts of data to make inferences about an individual, forensic genealogy exposes one’s identity by using only one other individual’s information. This paper echoes existing discussions that this peculiar situation where others’ information breaches one’s own privacy reveals the problematic nature of conceptualizing privacy relying only on individualistic assumptions. Moreover, this paper suggests a relational perspective as an alternative for theorizing privacy. Originality/value This situation that others’ information breached one’s own privacy calls for an updated understanding of not only privacy but also the relationship between the person and their information. Privacy scholars need to renew their ethical language and vocabularies to properly understand the issue, which recent privacy conceptualizations are already doing (e.g. group privacy). (shrink)

This dissertation consists of five chapters, each written as independent research papers that are unified by an overarching concern regarding information privacy and machine learning-based artificial intelligence (AI). This dissertation addresses the issues concerning privacy and AI by responding to the following three main research questions (RQs): RQ1. ‘How does an AI system affect privacy?’; RQ2. ‘How effectively does the General Data Protection Regulation (GDPR) assess and address privacy issues concerning both individuals and groups?’; and RQ3. ‘How can the value (...) of privacy be embedded into systems?’ -/- To respond to the RQs, this dissertation adopts the privacy impact assessment (PIA) as the overall methodology. A PIA encompasses three distinct stages; the first, the analytical stage, concerns the analysis of how AI (particularly focusing on inference as a process that includes inferred information, AI models’ performance, and accessing information uncovered by AI models) impacts privacy. Second, the legal assessment stage concerns whether AI that processes personal information and develops models complies with the GDPR. Finally, the design requirements stage features proposals for design requirements for systems aimed at protecting privacy. Accordingly, this dissertation is structured in three parts, each corresponding to a specific stage of a PIA and responding to one of the RQs. -/- Part I, which addresses the first stage of the PIA, comprises three chapters that altogether respond to RQ1. Chapter 2 analyses how AI impacts the descriptive aspect of privacy; this part argues that AI challenges the current definitions of privacy and that the ‘source control’ and ‘actual access’ definitions of privacy, once revised in the face of counter-examples involving inferred information, converge. Chapter 3, which considers how AI impacts the normative aspect of privacy, particularly the value of privacy, argues that AI affects the social value of privacy, which depends on trust, as this dimension of privacy is constituted when AI models perform accurately. Chapter 4 examines how AI impacts the normative aspect of privacy, particularly the right to privacy, and it argues that, although accessing information uncovered by AI models raises concerns about the privacy of algorithmically designed groups, the right to privacy cannot be recognised for such groups. This is the disruptive feature of AI that has led to consideration of new approaches other than the traditional one, which involves recognising the right to privacy, to protect the privacy of these groups. Instead of recognising the right to privacy for algorithmically designed groups, this chapter suggests taking a moral principle for the moral obligation of protecting vulnerable groups within an ethics of vulnerability. -/- Part II concerns the second stage of the PIA and consists of one chapter that responds to RQ2. Chapter 5, in addition to evaluating whether AI that processes personal information and develops models complies with the GDPR, also assesses whether the GDPR adequately addresses the privacy issues raised by AI. It specifically focuses on group privacy and argues that GDPR has limitations in protecting the privacy of algorithmically designed groups and that the privacy of such vulnerable entities must be considered in the context of privacy and data protection. -/- Part III, which is related to the third stage of the PIA, also consists of one chapter that responds to RQ3. Chapter 6 proposes design requirements to protect privacy by integrating privacy into systems and argues that privacy is instrumentally valuable for the sake of autonomy. Accordingly, to embed the value of privacy into systems, design requirements are articulated through the translation of norms that promote and protect autonomy. (shrink)