Biomedical research using data from participants’ mobile devices borrows heavily from the ethos of the “citizen science” movement, by delegating data collection and transmission to its volunteer subjects. This engagement gives volunteers the opportunity to feel like partners in the research and retain a reassuring sense of control over their participation. These virtues, in turn, give both grass-roots citizen science initiatives and institutionally sponsored mHealth studies appealing features to flag in recruiting participants from the public. But while grass-roots citizen science (...) projects are often community-based, mHealth research ultimately depends on the individuals who own and use mobile devices. This inflects the ethos of mHealth research towards a celebration of individual autonomy and empowerment, at the expense of its implications for the communities or groups to which its individual participants belong. But the prospects of group harms — and benefits — from mHealth research are as vivid as they are in other forms of data-intensive “precision health” research, and will be important to consider in the design of any studies using this approach. (shrink)

The emergence of Big Data has added a new aspect to conceptualizing the use of digital technologies in the delivery of public services and for realizing digital governance. This article explores, via the ‘value-chain’ approach, the evolution of digital governance research, and aligns it with current developments associated with data analytics, often referred to as ‘Big Data’. In many ways, the current discourse around Big Data reiterates and repeats established commentaries within the eGovernment research community. This body of knowledge provides (...) an opportunity to reflect on the ‘promise’ of Big Data, both in relation to service delivery and policy formulation. This includes, issues associated with the quality and reliability of data, from mixing public and private sector data, issues associated with the ownership of raw and manipulated data, and ethical issues concerning surveillance and privacy. These insights and the issues raised help assess the value of Big Data in government and smart city environments. (shrink)

PurposeThis paper aims to demonstrate the relevance of worldviews of the global south to debates of artificial intelligence, enhancing the human rights debate on artificial intelligence (AI) and critically reviewing the paper of UNESCO Commission on the Ethics of Scientific Knowledge and Technology (COMEST) that preceded the drafting of the UNESCO guidelines on AI. Different value systems may lead to different choices in programming and application of AI. Programming languages may acerbate existing biases as a people’s worldview is captured in (...) its language. What are the implications for AI when seen from a collective ontology? Ubuntu (I am a person through other persons) starts from collective morals rather than individual ethics.Design/methodology/approachLiterature overview on the African philosophy of Ubuntu as applied to artificial intelligence. Application of it to the United Nations Educational, Scientific and Cultural Organisation (UNESCO) debates on establishing guidelines to the ethics of artificial intelligence.FindingsMetaphysically, Ubuntu and its conception of social personhood (attained during one’s life) largely rejects transhumanism. When confronted with economic choices, Ubuntu favors sharing above competition and thus an anticapitalist logic of equitable distribution of AI benefits, humaneness and nonexploitation. When confronted with issues of privacy, Ubuntu emphasizes transparency to group members, rather than individual privacy, yet it calls for stronger (group privacy) protection. In democratic terms, it promotes consensus decision-making over representative democracy. Certain applications of AI may be more controversial in Africa than in other parts of the world, like care for the elderly, that deserve the utmost respect and attention, and which builds moral personhood. At the same time, AI may be helpful, as care from the home and community is encouraged from an Ubuntu perspective. The report on AI and ethics of the UNESCO World COMEST formulated principles as input, which are analyzed from the African ontological point of view. COMEST departs from “universal” concepts of individual human rights, sustainability and good governance which are not necessarily fully compatible with relatedness, including future and past generations. Next to rules based approaches, which may hamper diversity, bottom-up approaches are needed with intercultural deep learning algorithms.Research limitations/implicationsThere is very few existing literature on AI and Ubuntu. Therefore, this paper is of an explorative nature.Practical implicationsThe ethics of Ubuntu offers unique vantage points in looking at the organization of society and economics today, which are also relevant for development of AI, especially in its tenet of relatedness rather than individuality (and practical use of AI for individuals), taking responsibility for society as a whole (such as analyzing the benefit of AI for all strata of society), and embodying true inclusiveness. Whether looking at top-down guidelines for the development and implementation of AI or the bottom-up ethical learning process of AI (deep learning), ethics of the Global South can have an important role to play to combat global individualist tendencies and inequity, likely reinforced by AI. This warrants far more research.Social implicationsApplications of AI in Africa are not contextualized, do not address the most pressing needs of the African continent, lead to cybersecurity issues and also do not incorporate African ethics. UNESCO’s work in this regard is important but expert inputs are largely centered around Western “universal” principles and Organisation for Economic Cooperation and Development and EU precedence. African ethics have, so far, a small role to play in global ethics and philosophy and therefore risk to be overlooked in the discussion on AI and ethics. This is why the consultation process of UNESCO on ethics of AI was of paramount importance. However, it does not automatically lead to consultation of African philosophers or sages, as many are educated in Western (ized) education systems. See further details under practical implications.Originality/valueThis is a new area of research in which little work has been done so far. This paper offers the opportunity to widen the debate on AI and ethics beyond the conventional discourse, involving multiple worldviews, of which Ubuntu is just one. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...) concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

Data activism, promoting new forms of civic and political engagement, has emerged as a response to problematic aspects of datafication that include tensions between data openness and data ownership, and asymmetries in terms of data usage and distribution. In this article, we discuss MyData, a data activism initiative originating in Finland, which aims to shape a more sustainable citizen-centric data economy by means of increasing individuals' control of their personal data. Using data gathered during long-term participant-observation in collaborative projects with (...) data activists, we explore the internal tensions of data activism by first outlining two different social imaginaries – technological and socio-critical – within MyData, and then merging them to open practical and analytical space for engaging with the socio-technical futures currently in the making. While the technological imaginary favours data infrastructures as corrective measures, the socio-critical imaginary questions the effectiveness of technological correction. Unpacking them clarifies the kinds of political and social alternatives that different social imaginaries ascribe to the notions underlying data activism, and highlights the need to consider the social structures in play. The more far-reaching goal of our exercise is to provide practical and analytical resources for critical engagement in the context of data activism. By merging technological and socio-critical imaginaries in the work of reimagining governing structures and knowledge practices alongside infrastructural arrangements, scholars can depart from the most obvious forms of critique, influence data activism practice, and formulate data ethics and data futures. (shrink)

Over the past few years, there has been a proliferation of artificial intelligence strategies, released by governments around the world, that seek to maximise the benefits of AI and minimise potential harms. This article provides a comparative analysis of the European Union and the United States’ AI strategies and considers the visions of a ‘Good AI Society’ that are forwarded in key policy documents and their opportunity costs, the extent to which the implementation of each vision is living up to (...) stated aims and the consequences that these differing visions of a ‘Good AI Society’ have for transatlantic cooperation. The article concludes by comparing the ethical desirability of each vision and identifies areas where the EU, and especially the US, need to improve in order to achieve ethical outcomes and deepen cooperation. (shrink)

The debate on the ethics of privacy has been mainly dominated by Western perspectives, to the exclusion of broader ethical theories and socio-cultural perspectives. This imbalance carries risks; transplanted ethical norms and values can collide with those of the communities in which they are deployed. The consequent homogenization might also represent a missed opportunity to enrich and develop the current paradigm of privacy protection so as to effectively face new technological challenges. This article introduces and discusses the sub-Saharan philosophy of (...) Ubuntu and argues how its conception of the self helps to reinterpret some of the emerging issues revolving digital information technologies. To begin with, a general overview of the debate on the ethics of privacy is provided by distinguishing between individual and relational privacy. Also, the challenges of 'group privacy' are discussed. Then, we introduce basic principles of Ubuntu focusing on how these may have affected privacy conceptions and related legal practices. By outlining opportunities and risks of intercultural information ethics, we argue how Ubuntu—similarly to other communitarian moral philosophies—strengthens the development of the concept of relational privacy and, in particular, of group privacy. (shrink)

Digitization of a health record changes its accessibility. An electronic health record (EHR) can be accessed by multiple authorized users. Health information from EHRs contributes to learning healthcare systems’ development. The objective of this systematic review is to answer a question: What are ethical issues concerning research using EHRs in the literature? We searched Medline Ovid, Embase and Scopus for publications concerning ethical issues of research use of EHRs. We employed the constant comparative method to retrieve common ethical themes. We (...) descriptively summarized empirical studies. The study reveals the breadth, depth, and complexity of ethical problems associated with research use of EHRs. The central ethical question that emerges from the review is how to manage access to EHRs. Managing accessibility consists of interconnected and overlapping issues: streamlining research access to EHRs, minimizing risk, engaging and educating patients, as well as ensuring trustworthy governance of EHR data. Most of the ethical problems concerning EHR-based research arise from rapid cultural change. The framing of concepts of privacy, as well as individual and public dimensions of beneficence, are changing. We are currently living in the middle of this transition period. Human emotions and mental habits, as well as laws, are lagging behind technological developments. In the medical tradition, individual patient’s health has always been in the center. Transformation of healthcare care, its digitalization, seems to have some impacts on our perspective of health care ethics, research ethics and public health ethics. (shrink)

Often, when we share information about ourselves, we contribute to people learning personal things about others. This may happen because what we share about ourselves can be used to infer personal information about others. Such dependencies have become known as privacy dependencies in the literature. It is sometimes claimed that the scope of the right to privacy should be expanded in light of such dependencies. For example, some have argued that inferring information about others can violate their right to privacy. (...) Others have argued that sharing personal information about yourself that license such inferences can by itself violate the right to privacy. In this paper, we argue that the latter view should be rejected. (shrink)

Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that does not necessarily align (...) with classes or attributes already protected by privacy and anti-discrimination law or addressed in fairness- and discrimination-aware analytics. Individuals are linked according to offline identifiers and shared behavioural identity tokens, allowing for predictions and decisions to be taken at a group rather than individual level. This article examines the ethical significance of such ad hoc groups in analytics and argues that the privacy interests of algorithmically assembled groups in inviolate personality must be recognised alongside individual privacy rights. Algorithmically grouped individuals have a collective interest in the creation of information about the group, and actions taken on its behalf. Group privacy is proposed as a third interest to balance alongside individual privacy and social, commercial and epistemic benefits when assessing the ethical acceptability of analytics platforms. (shrink)

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)

Luciano Floridi was not the first to discuss the idea of group privacy, but he was perhaps the first to discuss it in relation to the insights derived from big data analytics. He has argued that it is important to investigate the possibility that groups have rights to privacy that are not reducible to the privacy of individuals forming such groups. In this paper, we introduce a distinction between two concepts of group privacy. The first, the “what happens in Vegas (...) stays in Vegas” privacy (in the following: WHVSV privacy), deals with confidential information shared with the member of a group and inaccessible to (all or a specific group of) outsiders. The second, to which we shall refer as inferential privacy, deals with the inferences that can be made about a group of people defined by a feature, or combination thereof, shared by all individuals in the group. We show why we unreservedly agree with Floridi that groups can have a form of privacy that amounts to more than the mere fact of being sets of individuals each of whom has individual privacy; moreover, like Floridi, we find it plausible that at least some groups (those satisfying our definition of type-a groups) may have a right to a species of group privacy (that is, WHVSV privacy) as groups (and not just as individuals who belong to those groups). However, by turning our attention to the context of big data analytics, we show that the relevant, new notion of group privacy is one of inferential privacy. We argue that an absolute right (either of individuals or groups) to inferential privacy is implausible. We also show that many groups generated algorithmically (those satisfying our definition of type-b groups) cannot be right holders as groups (unless they become type-a groups). (shrink)

This paper aims to provide new insights to debates on group privacy, which can be seen as part of a social turn in privacy scholarship. Research is increasingly showing that the classic individualistic understanding of privacy is insufficient to capture new problems in algorithmic and online contexts. An understanding of privacy as an “interpersonal boundary-control process” (Altman, The environment and social behavior, Brooks and Cole, Monterey, 1975) framing privacy as a social practice necessary to sustain intimate relationships is gaining ground. (...) In this debate, my research is focused on what I refer to as “self-determined groups” which can be defined as groups whose members consciously and willingly perceive themselves as being part of a communicative network. While much attention is given to new forms of algorithmically generated groups, current research on group privacy fails to account for the ways in which self-determined groups are affected by changes brought about by new information technologies. In an explorative case study on self-organized therapy groups, I show how these groups have developed their own approach to privacy protection, functioning on the basis of social practices followed by all participants. This informal approach was effective in pre-digital times, but online, privacy threats have reached a new level extending beyond the scope of a group’s influence. I therefore argue that self-determined sensitive topic groups are left facing what I present as a dilemma: a tension between the seemingly irreconcilable need for connectivity and a low threshold, on the one hand, and the need for privacy and trust, on the other. In light of this dilemma, I argue that we need new sorts of political solutions. (shrink)

Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed (...) consent process; and criteria for waivers of informed consent. In accordance with respect for persons and communities, investigators and IRBs should systematically consider potential group harm when designing and reviewing protocols, respectively. Research participants should be informed about any potential group harm in the consent process. We call for additional public discussion, empirical research, and normative analysis on these issues to determine the right regulatory and policy path forward. (shrink)

In various official documents, the European Union has declared its goal to pursue a citizen-centric governance of digital transformation. Through a critical review of several of these documents, here we show how “citizen-centric” is more a glamouring than a driving concept. De facto, the EU is enabling a federated data system that is corporate-driven, economic-oriented, and GDPR-compliant; in other words, a Digital Single Market (DSM). This leaves out societal and collective-level dimensions of digital transformation—such as social inclusion, digital sovereignty, and (...) environmental sustainability—which are acknowledged, but not operationalized, by the EU as pillars of a citizen-centric governance. Hence, the door is open to a complementary approach to the governance of digital transformation. We argue that, while a federated data model can constitute the tech-legal backbone of the emerging DSM, a commoning of data, as an ecosystemic approach that maintains a societal and collective outlook by default, can represent a complement to enact a truly citizen-centric governance. (shrink)

This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could—especially considering current developments in big data and artificial intelligence—compromise the right by leading to injustices and (...) affecting concrete modes of individual self-determination. In order to address this potential tension, two key elements for ethical reflection on data-driven medicine are proposed: the controllability of information flows, including technical infrastructures that are conducive towards controllability, and a paradigm shift towards output-orientation in governance and policy. (shrink)

Automated Influence is the use of Artificial Intelligence 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. (shrink)

Clicks, comments, transactions, and physical movements are being increasingly recorded and analyzed by Big Data processors who use this information to trace the sentiment and activities of markets and voters. While the benefits of Big Data have received considerable attention, it is the potential social costs of practices associated with Big Data that are of interest to us in this paper. Prior research has investigated the impact of Big Data on individual privacy rights, however, there is also growing recognition of (...) its capacity to be mobilized for surveillance purposes. Our paper delineates the underlying issues of privacy and surveillance and presents them as in tension with one another. We postulate that efforts at controlling Big Data may create a trade-off of risks rather than an overall improvement in data protection. We explore this idea in relation to the principles of the European Union’s General Data Protection Regulation as it arguably embodies the new ‘gold standard’ of cyber-laws. We posit that safeguards advocated by the law, anonymization and pseudonymization, while representing effective counter measures to privacy concerns, also incentivize the use, collection, and trade of behavioral and other forms of de-identified data. We consider the legal status of these ownerless forms of data, arguing that data protection techniques such as anonymization and pseudonymization raise significant concerns over the ownership of behavioral data and its potential use in the large-scale modification of activities and choices made both on and offline. (shrink)

The distinction between distributive and non-distributive profiles figures prominently in current evaluations of the ethical and epistemological risks that are associated with automated profiling practices. The diagnosis that non-distributive profiles may coincidentally situate an individual in the wrong category is often perceived as the central shortcoming of such profiles. According to this diagnosis, most risks can be retraced to the use of non-universal generalisations and various other statistical associations. This article develops a top-down analysis of non-distributive profiles in which this (...) fallibility of non-distributive profiles is no longer central. Instead, it focuses on how profiling creates various asymmetries between an individual data-subject and a profiler. The emergence of informational, interest, and perspectival asymmetries between data-subject and profiler explains how non-distributive profiles weaken the epistemic position of a profiled individual. This alternative analysis provides a more balanced assessment of the epistemic risks associated with non-distributive profiles. (shrink)

Recent advances in the capability of digital information technologies—particularly due to advances in artificial intelligence —have invigorated the debate on the ethical issues surrounding their use. However, this debate has often been dominated by ‘Western’ ethical perspectives, values and interests, to the exclusion of broader ethical and socio-cultural perspectives. This imbalance carries the risk that digital technologies produce ethical harms and lack social acceptance, when the ethical norms and values designed into these technologies collide with those of the communities in (...) which they are delivered and deployed. This special issue takes a step towards broadening the approach of digital ethics, by bringing together a range of cultural, social and structural perspectives on the ethical issues relating to digital information technology. Importantly, it refreshes and reignites the field of Intercultural Digital Ethics for the age of AI and ubiquitous computing. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts (...) associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (shrink)

Der vorliegende Artikel untersucht moderne Mensch-Maschine-Interaktion im Kontext verbreiteter Hard- und Softwareoberflächen und diskutiert davon ausgehend die Frage nach Aufklärung und Gegenaufklärung im digitalen Zeitalter. Er nimmt das Feld des »User Experience Designs« in den Blick - dies ist ein stilprägender Fachdiskurs, in dem verhaltenswissenschaftliche Erkenntnisse und massendatenbasierte Analysen zur Optimierung von Benutzeroberflächen und Interaktionsdesigns eingesetzt werden. Anhand von Beispielstudien wird argumentiert, dass dieser Gestaltung systematisch drei implizite anthropologische Annahmen zugrunde liegen: Nutzerverhalten gilt als durch prä-reflexive Stimuli beeinflussbar; es gilt (...) als statistisch ausmessbar und vorhersagbar; und Nutzer_innen wollen vermeintlich keine Einblicke in die technischen Abläufe hinter den bunten Bedienoberflächen digitaler Dienste erhalten. Die Diagnose der digitalen Entmündigung verweist sodann auf eine Gesamtheit von strukturellen Tendenzen, durch die Individuen der Fähigkeit zur selbstbestimmten Nutzung ihres Verstands in der Interaktion mit digitalen Artefakten beraubt werden. (shrink)

This paper analyses the role of information security in shaping the dissemination and re-use of biomedical data, as well as the embedding of such data in the material, social and regulatory landscapes of research. We consider the data management practices adopted by two UK-based data linkage infrastructures: the Secure Anonymised Information Linkage, a Welsh databank that facilitates appropriate re-use of health data derived from research and routine medical practice in the region; and the Medical and Environmental Data Mash-up Infrastructure, a (...) project bringing together researchers from the University of Exeter, the London School of Hygiene and Tropical Medicine, the Met Office and Public Health England to link and analyse complex meteorological, environmental and epidemiological data. Through an in-depth analysis of how data are sourced, processed and analysed in these two cases, we show that IS takes two distinct forms: epistemic IS, focused on protecting the reliability and reusability of data as they move across platforms and research contexts; and infrastructural IS, concerned with protecting data from external attacks, mishandling and use disruption. These two dimensions are intertwined and mutually constitutive, and yet are often perceived by researchers as being in tension with each other. We discuss how such tensions emerge when the two dimensions of IS are operationalised in ways that put them at cross purpose with each other, thus exemplifying the vulnerability of data management strategies to broader governance and technological regimes. We also show that whenever biomedical researchers manage to overcome the conflict, the interplay between epistemic and infrastructural IS prompts critical questions concerning data sources, formats, metadata and potential uses, resulting in an improved understanding of the wider context of research and the development of relevant resources. This informs and significantly improves the re-usability of biomedical data, while encouraging exploratory analyses of secondary data sources. (shrink)