Growing technological advances in intelligent artifacts and bitter experiences of the past have emphasized the need to use and operate ethics in this field. Accordingly, it is vital to discuss the ethical integrity of having intelligent artifacts. Concerning the method of gathering materials, the current study uses library and documentary research followed by attribution style. Moreover, descriptive analysis is employed in order to analyze data. Explaining and criticizing the opposing views in this field and reviewing the related literature, (...) it is concluded that the proof of the ethical integrity of the intelligent artifacts is possible due to the specific features of these artifacts and their own moral system. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the (...) foundation provided by Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (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)

Big Data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big Data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of Big Data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy (...) and the right to the protection of personal data in the respect of human dignity. DOI: 10.13140/RG.2.2.30867.43041. (shrink)

An Introduction to data ethics, focusing on questions of privacy and personal identity in the economic world as it is defined by big data technologies, artificial intelligence, and algorithmic capitalism. -/- Originally published in The Business Ethics Workshop, 3rd Edition, by Boston Acacdemic Publishing / FlatWorld Knowledge.

The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and (...) on the free movement of such data, and repealing Directive 95/46/EC (Data Protection Directive - General Data Protection Regulation, "GDPR"). The Ethics Issues section details the specific aspects of Big Data. After a brief section of Big Data Research, I finalize my work with the presentation of Conclusions on research ethics in working with Big Data. -/- ISBN 978-606-033-307-4 , DOI: 10.58679/MM14975 . (shrink)

In this review, we present some ethical imperatives observed in this pandemic from a data ethics perspective. Our exposition connects recurrent ethical problems in the discipline, such as, privacy, surveillance, transparency, accountability, and trust, to broader societal concerns about equality, discrimination, and justice. We acknowledge data ethics role as significant to develop technological, inclusive, and pluralist societies. - - - Resumen: En esta revisión, exponemos algunos de los imperativos éticos observados desde la ética de datos en (...) esta pandemia. Nuestra exposición busca conectar problemas éticos típicos dentro de esta disciplina, a saber, privacidad, vigilancia, transparencia, responsabilidad y confianza, con preocupaciones a nivel social relacionadas con la igualdad, discrimi nación y justicia. Consideramos que la ética de datos tiene un rol significativo para desarrollar sociedades tecnificadas, inclusivas y pluralistas. (shrink)

News about personal data breaches or data abusive practices, such as Cambridge Analytica, has questioned the trustworthiness of certain actors in the control of personal data. Innovations in the field of personal information management systems to address this issue have regained traction in recent years, also coinciding with the emergence of new decentralized technologies. However, only with ethically and legally responsible developments will the mistakes of the past be avoided. This contribution explores how current data management (...) schemes are insufficient to adequately safeguard data subjects, and in particular, it focuses on making these data flows transparent to provide an adequate level of accountability. To showcase this, and with the goal of enhancing transparency to foster trust, this paper investigates solutions for standardizing machine-readable policies to express personal data processing activities and their application to decentralized personal data stores as an example of ethical, legal, and technical responsible innovation in this field. (shrink)

In this essay, we advocate that the issue of health data ethics should no longer be considered on the level of individual scientists or research labs, but rather as a problem involving all stakeholders, from publishers, funders, ethical committees to governments, for the sake of research integrity.

In previous works (Floridi 2018) I introduced the distinction between hard ethics (which may broadly be described as what is morally right and wrong independently of whether something is legal or illegal), and soft or post-compliance ethics (which focuses on what ought to be done over and above existing legislation). This paper analyses the applicability of soft ethics to the General Data Protection Regulation and advances the theory that soft ethics has a dual advantage—as both (...) an opportunity strategy and a risk management solution. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the (...) Science, Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and ethicality (...) on data scientists, or data engineer during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical about Big Data research seriously and how to communicate messages designed to build trust in specific big data projects and the institution of science in general. This chapter explores the implications of various examples of engaging the public in online activities such as Wikipedia that contrast with (...) “Notice and Consent” forms and offers models for scientists to consider when approaching their potential subjects in research. Drawing from Lessig, Code and Other Laws of Cyberspace, the chapter suggests that four main regulators drive the shape of online activity: Code (or Architecture), Laws, Markets, and Norms. Specifically, scientists should adopt best practices in protecting computerized Big Data (Code), remain completely transparent about their data management practices (Law), make smart choices when deploying digital solutions that place a premium on information protection (Market), and, critically, portray themselves to the public as seriously concerned with protecting the privacy of persons and security of data (Norms). The community of Big Data users and collectors should remember that such data are not just “out there” somewhere but are the intimate details of the lives of real persons who have just as deep an interest in protecting their privacy as they do in the good work that is conducted with such data. (shrink)

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. (...) These emphasize increasing organizational flexibility, knowledge, and capacity, and reducing hazard. (shrink)

The predominant view is that a study using health data is observational research and should require individual consent unless it can be shown that gaining consent is impractical. But recent arguments have been made that citizens have an ethical obligation to share their health information for research purposes. In our view, this obligation is sufficient ground to expand the circumstances where secondary use research with identifiable health information is permitted without explicit subject consent. As such, for some studies the (...) Institutional Review Board/Research Ethics Committee review process should not assess the practicality of gaining consent for data use. Instead the review process should focus on assessing the public good of the research, public engagement and transparency. (shrink)

Compartmentalizing our distinct personal identities is increasingly difficult in big data reality. Pictures of the person we were on past vacations resurface in employers’ Google searches; LinkedIn which exhibits our income level is increasingly used as a dating web site. Whether on vacation, at work, or seeking romance, our digital selves stream together. One result is that a perennial ethical question about personal identity has spilled out of philosophy departments and into the real world. Ought we possess one, unified (...) identity that coherently integrates the various aspects of our lives, or, incarnate deeply distinct selves suited to different occasions and contexts? At bottom, are we one, or many? The question is not only palpable today, but also urgent because if a decision is not made by us, the forces of big data and surveillance capitalism will make it for us by compelling unity. Speaking in favor of the big data tendency, Facebook’s Mark Zuckerberg promotes the ethics of an integrated identity, a single version of selfhood maintained across diverse contexts and human relationships. This essay goes in the other direction by sketching two ethical frameworks arranged to defend our compartmentalized identities, which amounts to promoting the dis-integration of our selves. One framework connects with natural law, the other with language, and both aim to create a sense of selfhood that breaks away from its own past, and from the unifying powers of big data technology. (shrink)

In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of Data Science (...) provided in the scholarly literature includes “the study of the generalizable extraction of knowledge from data” (Dhar 2013, 64), “data stewardship and data sharing…access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research” (Hartter et al., 2013, 1), and “personal information, health status, daily activities and shopping preferences that are recorded and used to give us instant feedback and recommendations based on previous online behavior.” (Shin 2013) United States universities have begun to offer graduate programs in “data science”, anticipating the growth of this field for marketing, national security, and health industries. These universities include New York University, Columbia University, Stanford, Northwestern, and Syracuse. (shrink)

This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, similar to the way in which they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute at the University of Oxford. Ten arguments are provided to support the need to foster posthumous (...) medical data donation. Two major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to secure informed consent is rejected, and instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks, and offers five foundational principles for ethical medical data donation suggested as a draft code. (shrink)

For smart cities, data-driven innovation promises societal benefits and increased well-being for residents and visitors. At the same time, the deployment of data-driven innovation poses significant ethical challenges. Although cities and other public-sector actors have increasingly adopted ethical principles, employing them in practice remains challenging. In this commentary, we use a virtue-based approach that bridges the gap between abstract principles and the daily work of practitioners who engage in and with data-driven innovation processes. Inspired by Aristotle, we (...) describe practices of data-driven innovation in a smart city applying the concepts of virtue and phronêsis, meaning good judgment of and sensitivity to ethical issues. We use a dialogic case-study approach to study two cases of data-driven innovation in the city of Helsinki. We then describe as an illustration of how our approach can help bridge the gap between concrete practices of data-driven innovation and high-level principles. Overall, we advance a theoretically grounded, virtue-based approach, which is practice oriented and linked to the daily work of data scientists and other practitioners of data-driven innovation. Further, this approach helps understand the need for and importance of individual application of phronêsis, which is particularly important in public-sector organizations that can experience gaps between principle and practice. This importance is further intensified in cases of data-driven innovation in which, by definition, novel and unknown contexts are explored. (shrink)

This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated in the context (...) of related discussions, but their distinctively ethical dimensions have not been explored in much detail. Our goal here is to articulate and clarify these ethical challenges, while also highlighting areas where these issues intersect and overlap. Ultimately, responsible data-driven policing requires collaboration between communities, academics, technology developers, police departments, and policy-makers to confront and address these challenges. And as we will see, it may also require critically reexamining the role and value of police in society. (shrink)

This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, in a way similar to how they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute. Ten arguments are provided to support the need to foster posthumous medical data donation. Two (...) major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to ask for informed consent is rejected, and it instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks providing five foundational principles for ethical medical data donation; and suggesting a draft for such a code. (shrink)

According to Facebook’s Mark Zuckerberg, big data reality means, “The days of having a different image for your co-workers and for others are coming to an end, which is good because having multiple identities represents a lack of integrity.” Two sets of questions follow. One centers on technology and asks how big data mechanisms collapse our various selves (work-self, family-self, romantic-self) into one personality. The second question set shifts from technology to ethics by asking whether we want (...) the kind of integrity that Zuckerberg lauds, and that big data technology enables. The negative response is explored by sketching three ethical conceptions of selfhood that recommend personal identity be understood as dis-integrating. The success of the strategies partially depends upon an undermining use of big data platforms. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

The COVID-19 pandemic demonstrated the benefits of international data sharing. Data sharing enabled the health care policy makers to make decisions based on real-time data, it enabled the tracking of the virus, and importantly it enabled the development of vaccines that were crucial to mitigating the impact of the virus. This data sharing is not the norm as data sharing needs to navigate complex ethical and legal rules, and in particular, the fragmented application of the (...) General Data Protection Regulation (GDPR). The introduction of the draft regulation for a European Health Data Space (EHDS) in May 2022 seeks to address some of these legal issues. If passed, it will create an obligation to share electronic health data for certain secondary purposes. While there is a clear need to address the legal complexities involved with data sharing, it is critical that any proposed reforms are in line with ethical principles and the expectations of the data subjects. In this paper we offer a critique of the EHDS and offer some recommendations for this evolving regulatory space. (shrink)

Using speech act theory from the Philosophy of Language, this paper attempts to develop an ethical framework for the phenomenon of speech processing. We use the concepts of the illocutionary force and the illocutionary content of a speech act to explain the ethics of speech processing. By emphasizing the different stages involved in speech processing, we explore the distinct ethical issues that arise in relation to each stage. Input, processing, and output are the different ethically relevant stages under which (...) a spoken item or a speech navigates within the range of speech-processing modules. Employing the illocutionary force-content distinction, we specify and characterize the input-related ethical issues, the output-related ethical issues, and the processing-related ethical issues involved in speech processing. Together with illocutionary force-content distinction, we employ the data-information distinction to characterize the stage-wise ethical issues in the phenomenon of speech processing as the ethics of collecting (speech) data, the ethics of contextualizing (speech) data/information, and the ethics of releasing the con-textualized information (processed speech). Immediate ethical issues that arise from the range of speech processing modules are distinguished from distant ethical issues. We also indicate the nature of ethical issues that arise from Speaker Independent speech technologies. (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)

This article addresses the general ethical issues of accessing online personal data for research purposes. The authors discuss the practical aspects of online research with a specific case study that illustrates the ethical challenges encountered when accessing data from Kooth, an online youth web-counselling service. This paper firstly highlights the relevance of a process-based approach to ethics when accessing highly sensitive data and then discusses the ethical considerations and potential challenges regarding the accessing of public (...) class='Hi'>data from Digital Mental Health services. It presents solutions that aim to protect young DMH service users as well as the DMH providers and researchers mining such data. Special consideration is given to service users’ expectations of what their data might be used for, as well as their perceptions of whether the data they post is public, private or open. We provide recommendations for planning and designing online research in an ethical manner that includes vulnerable young people as research participants. We emphasise the distinction between public, private and open data, which is crucial to comprehend the ethical challenges in accessing DMH data. Among our key recommendations, we foreground the need to consider a collaborative approach with the DMH providers while respecting service users’ control over personal data, and we propose the implementation of digital solutions embedded within the platform for explicit opt-out/opt-in recruitment strategies and ‘read more’ options. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to (...) work on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

Appropriate information and consent has been one of the most intensely discussed topics within the context of biobank research. In parallel to the normative debate, many socio-empirical studies have been conducted to gather experiences, preferences and views of patients, healthy research participants and further stakeholders. However, there is scarcity of literature which connects the normative debate about justifications for different consent models with findings gained in empirical research. In this paper we discuss findings of a limited review of socio-empirical research (...) on patients’ and healthy research participants’ experiences and views regarding consent to biobank research in light of ethical principles for appropriate information and consent. (shrink)

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we (...) have to reach a stage in the development of data analytics where groups are protected as entities. This chapter offers a deeper examination of harms to the groups. (shrink)

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the (...) last few years have witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

Biodiversity science is in a pivotal period when diverse groups of actors—including researchers, businesses, national governments, and Indigenous Peoples—are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. These repositories, often called biodiversity data portals, are a type of organization for which governance can address or perpetuate the colonial history of biodiversity science and current inequities. Researchers and Indigenous Peoples are developing and implementing new strategies to examine and change assumptions about which agents should count (...) as salient participants in scientific projects, especially in projects that build and manage large digital data portals. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Principles for scientific data management and governance. To characterize how these principles influence the governance of biodiversity data portals, we develop an account of fit-for-use data that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity researchers, prioritize machine agents and efficient computation, while the CARE Principles prioritize Indigenous Peoples and their data sovereignty. Both illustrate the potency of an emerging general strategy by which groups of actors craft and implement governance principles for data fitness-of-use to change assumptions about who are salient participants in data science. (shrink)

Surveillance studies scholars and privacy scholars have each developed sophisticated, important critiques of the existing data-driven order. But too few scholars in either tradition have put forward alternative substantive conceptions of a good digital society. This, I argue, is a crucial omission. Unless we construct new “sociotechnical imaginaries,” new understandings of the goals and aspirations digital technologies should aim to achieve, the most surveillance studies and privacy scholars can hope to accomplish is a less unjust version of the technology (...) industry’s own vision for the future. (shrink)

The European Medical Information Framework project, funded through the IMI programme, has designed and implemented a federated platform to connect health data from a variety of sources across Europe, to facilitate large scale clinical and life sciences research. It enables approved users to analyse securely multiple, diverse, data via a single portal, thereby mediating research opportunities across a large quantity of research data. EMIF developed a code of practice to ensure the privacy protection of data subjects, (...) protect the interests of data sharing parties, comply with legislation and various organisational policies on data protection, uphold best practices in the protection of personal privacy and information governance, and eventually promote these best practices more widely. EMIF convened an Ethics Advisory Board, to provide feedback on its approach, platform, and the EcoP. The most important challenges the ECoP team faced were: how to define, control and monitor the purposes for which federated health data are used; the kinds of organisation that should be permitted to conduct permitted research; and how to monitor this. This manuscript explores those issues, offering the combined insights of the EAB and EMIF core ECoP team. For some issues, a consensus on how to approach them is proposed. For other issues, a singular approach may be premature but the challenges are summarised to help the community to debate the topic further. Arguably, the issues and their analyses have application beyond EMIF, to many research infrastructures connected to health data sources. (shrink)

ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place them (...) at a disadvantage to the majority of citizens; Table 3 in this guide presents some of the many forms and causes of vulnerability. As a result, vulnerable people need greater support to navigate the digital world, and to ensure that they are able to exercise their rights. This guide explains where such support can be found, and also answers the following questions: - What are the main ethical and legal issues around ICTs for vulnerable citizens? - Who is vulnerable in Europe? - How do issues around ICTs affect vulnerable people in particular? This guide is a resource for members of vulnerable groups, people who work with vulnerable groups, and citizens more broadly. It is also useful for data controllers1 who collect data about vulnerable citizens. While focused on citizens in Europe, it may be of interest to people in other parts of the world. It forms part of the Citizens’ Information Pack produced by the PANELFIT project, and is available in English, French, German, Italian and Spanish. You are welcome to translate this guide into other languages. Please send us a link to online versions in other languages, so that we can add them to the project website. (shrink)

The word data (sing. datum) is originally Latin for “things given or granted”. Because of such a humble and generic meaning, the term enjoys considerable latitude both in its technical and in its common usage, for almost anything can be referred to as a “thing given or granted” (Cherry [1978]). With some reasonable approximation, four principal interpretations may be identified in the literature. The first three captures part of the nature of the concept and are discussed in the next (...) section. The fourth is the most fundamental and satisfactory, so it is discussed separately, in section three. On its basis, some further clarifications about the nature of data are introduced in sections four to six. A reminder about the social, legal and ethical issues raised by the use of data concludes this entry. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the (...) recent work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

Due to ethical lapses of leaders, interest in ethical leadership has grown, raising important questions about the responsibility of leaders in ensuring moral and ethical conduct. Research conducted on ethical leadership failed to investigate the active role that the characteristics of ethical climate and organisational justice have an increasing or decreasing influence on the ethical leadership in the organisation’s outcomes of employees’ ethical behaviour. Thus, this study examined the dual-mediations of work ethical climate and organisational justice on the relation of (...) ethical leadership and ethical behaviour of employees. A total of 620 full-time employees from five Iraqi provinces working at 33 Iraqi organisations in the field of manufacturing, retailing, medical, insurance, information technology, legal, finance, and telecommunication responded to the questionnaire survey. Structural equation modelling (SEM) was used to test the model and data analysis was carried out using structural equation modelling-partial least square (SEM-PLS). The results revealed that there is a significant relationship between ethical leadership behaviour and the ethical behaviour of employees. Primarily, the study also found that ethical climate and organisational justice play a very significant mediating role between ethical leadership and employees’ ethical behaviour. (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)

The ability to collect fine-grained energy data from smart meters has benefits for utilities and consumers. However, a proactive approach to data privacy is necessary to maximize the potential of these data to support low-carbon energy systems, and innovative business models.

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between (...) the theory of AI ethics principles and the practical design of AI systems. In previous work , we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service’. (shrink)

The paper investigates the ethics of information transparency (henceforth transparency). It argues that transparency is not an ethical principle in itself but a pro-ethical condition for enabling or impairing other ethical practices or principles. A new definition of transparency is offered in order to take into account the dynamics of information production and the differences between data and information. It is then argued that the proposed definition provides a better understanding of what sort of information should be disclosed (...) and what sort of information should be used in order to implement and make effective the ethical practices and principles to which an organisation is committed. The concepts of “heterogeneous organisation” and “autonomous computational artefact” are further defined in order to clarify the ethical implications of the technology used in implementing information transparency. It is argued that explicit ethical designs, which describe how ethical principles are embedded into the practice of software design, would represent valuable information that could be disclosed by organisations in order to support their ethical standing. (shrink)

This article addresses the question of how ‘Country of Origin Information’ reports—that is, research developed and used to support decision-making in the asylum process—can be published in an ethical manner. The article focuses on the risk that published COI reports could be misused and thereby harm the subjects of the reports and/or those involved in their development. It supports a situational approach to assessing data ethics when publishing COI reports, whereby COI service providers must weigh up the benefits (...) and harms of publication based, inter alia, on the foreseeability and probability of harm due to potential misuse of the research, the public good nature of the research, and the need to balance the rights and duties of the various actors in the asylum process, including asylum seekers themselves. Although this article focuses on the specific question of ‘how to publish COI reports in an ethical manner’, it also intends to promote further research on data ethics in the asylum process, particularly in relation to refugees, where more foundational issues should be considered. (shrink)

Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my (...) central analog will lead us to a decision-theoretic analogy for much more systematic documentation of decisions in clinical trials. My conclusion is twofold: every DSMB decision should articulate a clear general principle (a ratio decidendi) that gives reason for the decision; and all such decisions should be made public. I use Bartha’s (2010) theory of analogical arguments to both frame and assess my argument by analogy. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare (...) as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)