Purpose As data-driven tools increasingly shape our life and tech ethics crises become strikingly frequent, data ethics coursework is urgently needed. The purpose of this study is to map the field of data ethics curricula, tracking relations between courses, instructors, texts and writers, and present a proof-of-concept interactive website for exploring these relations. This method is designed to be used in curricular research and development and provides multiple vantage points on this multidisciplinary field. Design/methodology/approach (...) The authors use data science methods to foster insights about the field of data ethics education and literature. The authors present a semantic, linked open data graph in the Resource Description Framework, along with proof-of-concept analyses and an exploratory website. Its framework is open-source and language-agnostic, providing the seed for future contributions of code, syllabi and resources from the global data ethics community. Findings This method provides a convenient means of exploring an overview of the field of data ethics’ social and textual relations. For educators designing or refining a course, the authors provide a method for curricular introspection and discovery of transdisciplinary curricula. Research limitations/implications The syllabi the authors have collected are self-selected and represent only a subset of the field. Furthermore, this method exclusively represents a course’s assigned literature rather than a holistic view of what courses teach. The authors present a prototype rather than a finished product. Originality/value This curricular survey provides a new way of modeling a field of study, using existing ontologies to organize graph data into a comprehensible overview. This framework may be repurposed to map the institutional knowledge structures of other disciplines, as well. (shrink)

This open access book explains how leading business organizations attempt to achieve the responsible and ethical use of artificial intelligence (AI) and other advanced information technologies. These technologies can produce tremendous insights and benefits. But they can also invade privacy, perpetuate bias, and otherwise injure people and society. To use these technologies successfully, organizations need to implement them responsibly and ethically. The question is: how to do this? Data ethics management, and this book, provide some answers. -/- The (...) authors interviewed and surveyed data ethics managers at leading companies. They asked why these experts see data ethics as important and how they seek to achieve it. This book conveys the results of that research on a concise, accessible way. -/- Much of the existing writing on data and AI ethics focuses either on macro-level ethical principles, or on micro-level product design and tooling. The interviews showed that companies need a third component: data ethics management. This third element consists of the management structures, processes, training and substantive benchmarks that companies use to operationalize their high-level ethical principles and to guide and hold accountable their developers. Data ethics management is the connective tissue makes ethical principles real. It is the focus of this book. -/- This book should be of use to organizations that wish to improve their own data ethics management efforts, legislators and policymakers who hope to build on existing management practices, scholars who study beyond compliance business behavior, and members of the public who want to understand better the threats that AI poses and how to reduce them. (shrink)

The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. We are moving towards changes in how ethics has to be perceived: away from individual decisions with specific and knowable outcomes, towards actions by many unaware that they may have taken actions with unintended consequences for anyone. Responses will require a rethinking of ethical choices, the lack (...) thereof and how this will guide scientists, governments, and corporate agencies in handling Big Data. This essay elaborates on the ways Big Data impacts on ethical conceptions. (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 contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present (...) the DEDA framework as an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices. (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)

In discourses on digitization and the data economy, it is often claimed that data subjects shall beownersof their data. In this paper, we provide a problem diagnosis for such calls fordata ownership: a large variety of demands are discussed under this heading. It thus becomes challenging to specify what—if anything—unites them. We identify four conceptual dimensions of calls for data ownership and argue that these help to systematize and to compare different positions. In view of this (...) pluralism of data ownership claims, we introduce, spell out and defend a constructive interpretative proposal: claims for data ownership are charitably understood as attempts to call for theredistributionof material resources and the socio-culturalrecognitionof data subjects. We argue that as one consequence of this reading, it misses the point to reject claims for data ownership on the grounds that property in data does not exist. Instead, data ownership brings to attention a claim to renegotiate such aspects of thestatus quo. (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.

A self-perceived lack of training in ethical theories and related pedagogy has kept many engineering faculty members from teaching data ethics, an important aspect of engineering research that has become more salient in recent years. This paper describes the development of a module, which includes concepts, cases, policies, and best practices, to support the teaching of ethical data practice. Based on a user-oriented design approach and a moral literacy framework, the module was designed to be used in (...) different courses and co-curricular activities for students of varying levels of competence to improve their ability to identify and analyze ethical problems associated with the handling of research data. This work seeks to encourage ethical reflection on researchers’ data practice through the idea of an “ecology of data,” which highlights the co-production of data by multiple, interconnected, and heterogeneous actors. This paper also presents online and in-class evidence about the impact and limitations of the module, which is now available for interested researchers and instructors to browse and use. (shrink)

Omitting experimental data is often considered a violation of scientific integrity. If we consider experimental inquiry as a questioning process, omitting data is seen to be merely an example of tentatively rejecting (‘bracketing’) some of nature’s answers. Such bracketing is not only occasionally permissible; sometimes it is mandated by optimal interrogative strategies. When to omit data is therefore a strategic rather than ethical question. These points are illustrated by reference to Millikan’s oil drop experiment.

We live in the Information Age. Advances over the past 50 years in computing technology have enabled ever-expanding capacity to generate, store, transfer, process and analyse information about people, societies, products, services, the environment—nearly every aspect of the world. In parallel, concerns over how such data is being used have emerged, focusing especially on issues of privacy and confidentiality. Yet as technological capabilities continue to expand, the debate over ethical uses of data inevitably has evolved as well. Whereas (...) we used to just talk about data, now there is great concern over Big Data. This chapter explores what, exactly, Big Data is; the ethical ramifications of its emergence, particularly for consent and anonymization; and what sorts of policy solutions are being proposed to address these emerging challenges. We will not defend one particular approach, but hope this chapter can be a useful reference for those who are contemplating difficult questions surrounding responsible management of Big Data. (shrink)

McCoy and colleagues (2023) offer a reflective framework for data ethics and governance with several historical bioethics principles as a foundation. Their framework is one among a plethora of othe...

The Catholic moral tradition has a rich foundation that applies broadly to encompass all areas of human experience. Yet, there is comparatively little in Catholic thought on the ethics of the collection and use of data, especially in healthcare. We provide here a brief overview of terminology, concepts, and applications of data in the context of healthcare, summarize relevant theological principles and themes (including the Vatican’s Rome Call for AI Ethics), and offer key questions for ethicists (...) and data managers to consider as they analyze ethical implications pertinent to data governance and data management. (shrink)

Data science skills are rapidly becoming a necessity in modern science. In response to this need, institutions and organizations around the world are developing research data science curricula to teach the programming and computational skills that are needed to build and maintain data infrastructures and maximize the use of available data. To date, however, few of these courses have included an explicit ethics component, and developing such components can be challenging. This paper describes a novel (...) approach to teaching data ethics on short courses developed for the CODATA-RDA Schools for Research Data Science. The ethics content of these schools is centred on the concept of open and responsible science citizenship that draws on virtue ethics to promote ethics of practice. Despite having little formal teaching time, this concept of citizenship is made central to the course by distributing ethics content across technical modules. Ethics instruction consists of a wide range of techniques, including stand-alone lectures, group discussions and mini-exercises linked to technical modules. This multi-level approach enables students to develop an understanding both of “responsible and open science citizenship”, and of how such responsibilities are implemented in daily research practices within their home environment. This approach successfully locates ethics within daily data science practice, and allows students to see how small actions build into larger ethical concerns. This emphasises that ethics are not something “removed from daily research” or the remit of data generators/end users, but rather are a vital concern for all data scientists. (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)

The digital revolution has disruptively reshaped the way health services are provided and how research is conducted. This transformation has produced novel ethical challenges. The digitalization of...

In informationally mature societies, almost all organisations record, generate, process, use, share and disseminate data. In particular, the rise of AI and autonomous systems has corresponded to an improvement in computational power and in solving complex problems. However, the resulting possibilities have been coupled with an upsurge of ethical risks. To avoid the misuse, underuse, and harmful use of data and data-based systems like AI, we should use an ethical framework appropriate to the object of its reasoning. (...) Unfortunately, in recent years, the space for data-related ethics has not been precisely defined in organisations. As a consequence, there has been an overlapping of responsibilities and a void of clear accountabilities. Ethical issues have, therefore, been dealt with using inadequate levels of abstraction (e.g. legal, technical). Yet, if building an ethical infrastructure requires the collaboration of each body, addressing ethical issues related to data requires leaving room for the appropriate level of abstraction. This paper first aims to show how the space of data ethics is already latent in organisations. It then highlights how to redefine roles (chief data ethics officer, data ethics committee, etc.) and codes (code of data ethics) to create and maintain an environment where ethical reasoning about data, information, and AI systems may flourish. (shrink)

This book offers guidance and the current scholarship surrounding institutional review boards policies and procedures, human research protections for researchers and principal investigators, data privacy, data ethics, and other areas of interest related to human subjects research.

After a brief overview of ethical issues in an Australian context catalyzed by the current pandemic, this article focuses on data protection in the light of recent debates about COVID-19 data tracking in Australia and globally. This article looks at the issue of trust as a fundamental principle of effective and ethical COVID-safe measures undertaken by the government. Key to ensuring such trust are Habermasian participatory dialogs, which assume trust as a condition of authentic illocution, and an emphasis (...) on short-term data capture. (shrink)

This review is based on an expanded version of the session outcome document we prepared for WSIS. The outcome was providing a summary of the session: Data Ethics and the Ethics of Digital and Emerging Technologies – Building Trust, Serving Humanity – Globethics, which was held a few days earlier from 16:00 to 16:45, Monday, 13 March 2023 at the Geneva International Conference Center, under the auspices of the ITU.

Genomic data is generated, processed and analysed at an increasingly rapid pace. This data is not limited to the medical context, but plays an important role in other contexts in society, such as commercial DNA testing, the forensic setting, archaeological research, and genetic surveillance. Genomic information also crosses the borders of these domains, e.g. forensic use of medical genetic information, insurance use of medical genomic information, or research use of commercial genomic data. This paper (1) argues that (...) an informed consent approach for genomic information has limitations in many societal contexts, and (2) seeks to broaden the bioethical debate on genomic information by suggesting an approach that is applicable across multiple societal contexts. I argue that the contextual integrity framework, a theory rooted in information technology and big data ethics, is an effective tool to explore ethical challenges that arise from genomic information within a variety of different contexts. Rather than focusing on individual control over information, the contextual integrity approach holds that information should be shared and protected according to the norms that govern certain distinct social contexts. Several advantages of this contextual integrity approach will be discussed. The paper concludes that the contextual integrity framework helps to articulate and address a broad spectrum of ethical, social, and political factors in a variety of different societal contexts, while giving consideration to the interests of individuals, groups, and society at large. (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)

Name der Zeitschrift: Jahrbuch für Wissenschaft und Ethik Jahrgang: 20 Heft: 1 Seiten: 277-288.

Although ethical guidelines for doing Internet research are available, most prominently those of the Association of Internet Researchers ( www.aoir.org ), ethical decision-making for research on publicly available, naturally-occurring data remains a major challenge. As researchers might also turn to others to inform their decisions, this article reviews recent research papers on publicly available, online data. Research involving forums such as Facebook pages, Twitter, YouTube, news comments, blogs, etc. is examined to see how authors report ethical considerations and (...) how they quote these data. We included 132 articles published in discourse analysis-oriented journals between January 2017 and February 2020. Roughly one third of the articles (85 out of 132) did not discuss ethical issues, mostly claiming the data were publicly available. Quotations nevertheless tended to be anonymized, although retrievability of posts was generally not taken into account. In those articles in which ethical concerns were reported, related decisions appeared to vary substantially. In most cases it was argued that informed consent was not required. Similarly, approval from research ethics committees was mostly regarded unnecessary. Other ethical issues like consideration of users’ expectations and intentions, freedom of choice, possible harm, sensitive topics, and vulnerable groups were rarely discussed in the articles. We argue for increased attention to ethical issues and legal aspects in discourse analytic articles involving online data beyond mentioning general concerns. Instead, we argue for more involvement of users/participants in ethical decision-making, for consideration of retrievability of posts and for a role for journal editors. (shrink)

Our ability to generate genomic data is currently well ahead of our ability to understand what they mean, raising challenges about how best to engage with them. This article considers ethical aspects of work with such data, focussing on research contexts that are intertwined with clinical care. We discuss the identifying nature of genomic data, the medical information intrinsic within them, and their linking of people within a biological family. We go on to consider what this means (...) for consent, the importance of thoughtful sharing of genomic data, the challenge of constructing meaningful findings, and the legacy of unequal representation in genomic datasets. We argue that the ongoing success of genomic data research relies on public trust in the enterprise: to justify this trust, we need to ensure robust stewarding, and wide engagement about the ethical issues inherent in such practices. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical (...) challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model (...) for facilitating this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of ‘reasonable expectations’ for the reuse of data; Waldron’s account of ‘integrity’ as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests. (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)

Research question and aim Clinical Ethics Committees (CECs) aim to support healthcare professionals (HPs) and healthcare organizations to deal with the ethical issues of clinical practice. In 2020, a CEC was established in an Oncology Research Hospital in the North of Italy. This paper describes the development process and the activities performed 20 months from the CEC’s implementation, to increase knowledge about CEC’s implementation strategy. Research design We collected quantitative data related to number and characteristics of CEC activities (...) carried out from October 2020 to June 2022 using the CEC internal database. Data were reported descriptively and compared with data from the literature to provide a complete overview of the CEC’s development and implementation process. Participants and research context The study has been conducted at the local health authority (LHA) of Reggio Emilia. It is a report of the activities provided by the CEC, where no HPs or patients were involved. Ethical Considerations The report is part of a larger study named EVAluating a Clinical Ethics Committee implementation process (EvaCEC), which has been approved by the Local Ethics Committee (AUSLRE Protocollo n° 2022/0026554 of 24/02/2022). EvaCEC is also the first author's PhD project. Findings In total, the CEC performed 7 ethics consultations (EC), published three policies related to particular ethical questions of clinical and organizational practice, provided one educational online course on ethics consultation targeting employed HPs, and promoted a specific dissemination process among the different departments of the LHA. According to our results, the CEC widely fulfilled the standard threefold set of clinical ethics support services tasks (namely, ethics consultation, ethics education, and policy development), but further investigations are needed to evaluate the CEC’s impact on clinical practice. Conclusion Our findings may increase knowledge regarding the composition, role, and tasks of a CEC in an Italian setting, informing future strategies and efforts to regulate these institutions officially. (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)

Public-private partnerships are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain—producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, public antipathy (...) to the commercialisation of public sector health data, and questions of ownership, both of the data and any resulting intellectual property or products. As a specific example we consider the case of the UK National Health Service providing patient data to Google’s DeepMind AI program to develop a diagnostic app for kidney disease. This article is an application of the framework presented in this issue of ABR. Please refer to that article for more information on 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. We use four specific values to help analysis these issues: public benefit, stewardship, transparency and engagement. We demonstrate how the Deliberative Framework can support ethical governance of PPPs involving biomedical big data. (shrink)

Big data, big impact -- Values and actions -- Current practices -- Aligning values and actions.

The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of (...) the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: informed consent, privacy, ownership, epistemology and objectivity, and ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: the dangers of ignoring group-level ethical harms; the importance of epistemology in assessing the ethics of Big Data; the changing nature of fiduciary relationships that become increasingly data saturated; the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; future problems with ownership of intellectual property generated from analysis of aggregated datasets; and the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices. (shrink)

Data infrastructure includes the bureaucratic, technical, and social mechanisms that assist in actions like data management, analysis, storage, and sharing. While issues like data sharing have been addressed in depth in bioethical literature, data infrastructure presents its own ethical considerations, apart from the actions (such as data sharing and data analysis) that it enables. This essay outlines some of these considerations—namely, the ethics of efficiency, the visibility of infrastructure, the power of standards, and (...) the impact of new technologies—in order to invite the bioethics community to participate in conversations about infrastructure, as their expertise is both needed and welcomed. (shrink)

Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding (...) big data, and problematic research can still meet ethical standards. While ethics seem concrete through institutional deployment, I frame ethics as produced. Informed by my ethnographic research at a large public university in the U.S., I explore ethics through two models: ethics as institutional procedures and ethics as acts and intentions. The university where I conducted fieldwork is the development grounds for a predictive model that uses student data to anticipate academic success. While students consent to data collection, the circumstances of consent and the degree to which they are informed are not so apparent, as many data are a product of creep. Drawing from interviews and participant observation with administrators, data scientists, developers, and students, I examine data ethics, from a larger institutional model to everyday enactments related to data creep. After demonstrating the limits of such models, I propose a remodeling of ethics that draws on recent works on data, justice, and refusal to pose generative questions for rethinking ethics in institutional contexts. (shrink)

The theories of information ethics articulated by Luciano Floridi and his collaborators have clear implications for law. Information law, including the law of privacy and of intellectual property, is especially likely to benefit from a coherent and comprehensive theory of information ethics. This article illustrates how information ethics might apply to legal doctrine, by examining legal questions related to the ownership and control of the personal data representations, including photographs, game avatars, and consumer profiles, that have (...) become ubiquitous with the proliferation of information and communication technologies. Recent controversy over the control of player performance statistics in “fantasy” sports leagues provides a limiting case for the analysis. Such data representations will in many instances constitute the kind of personal data that information ethics asserts constitutes an information entity. Legal doctrine in some instances proves sympathetic to such an assertion, but remains largely inchoate as to which data might constitute a given information entity in a given instance. Neither is information ethics, in its current state of development, entirely helpful in answering this critical question. While information ethics holds some promise to bring coherence to this area of the law, further work articulating a richer theory of information ethics will be necessary before it can do so. (shrink)

This paper investigates how deans and directors at the top 50 global MBA programs (as rated by the "Financial Times" in their 2006 Global MBA rankings) respond to questions about the inclusion and coverage of the topics of ethics, corporate social responsibility, and sustainability at their respective institutions. This work purposely investigates each of the three topics separately. Our findings reveal that: (1) a majority of the schools require that one or more of these topics be covered in their (...) MBA curriculum and one-third of the schools require coverage of all three topics as part of the MBA curriculum, (2) there is a trend toward the inclusion of sustainability-related courses, (3) there is a higher percentage of student interest in these topics (as measured by the presence of a Net Impact club) in the top 10 schools, and (4) several schools are teaching these topics using experiential learning and immersion techniques. We note a fivefold increase in the number of stand-alone ethics courses since a 1988 investigation on ethics, and we include other findings about institutional support of centers or special programs; as well as a discussion of integration, teaching techniques, and notable practices in relation to all three topics. (shrink)

A model of correlates of executives' views of organizational politics was presented. The model incorporated three sets of variables: executives' background, values and attitudes. Data collected from 302 managers were used to validate the model. The results showed that precursors of executive perceptions of the ethics and effect of company politics were different. Values were stronger than background variables in explaining executives' views of company politics.

In recent years, 'nudge units' or 'behavioral insights teams' have been created in the United States, the United Kingdom, Germany, and other nations. All over the world, public officials are using the behavioral sciences to protect the environment, promote employment and economic growth, reduce poverty, and increase national security. In this book, Cass R. Sunstein, the eminent legal scholar and best-selling co-author of Nudge, breaks new ground with a deep yet highly readable investigation into the ethical issues surrounding nudges, choice (...) architecture, and mandates, addressing such issues as welfare, autonomy, self-government, dignity, manipulation, and the constraints and responsibilities of an ethical state. Complementing the ethical discussion, The Ethics of Influence: Government in the Age of Behavioral Science contains a wealth of new data on people's attitudes towards a broad range of nudges, choice architecture, and mandates. (shrink)

In the age of rapid AI advancement, digital colonialism poses a significant threat to Indigenous communities, perpetuating inequalities and exploiting their data. This commentary delves into the concept of Indigenous data sovereignty as a powerful framework for resisting digital colonialism and promoting ethical AI development.

Faced with the exponential development of Big Data and both its legal and economic repercussions, we are still slightly in the dark concerning the use of digital information. In the perpetual balance between confidentiality and transparency, this data will lead us to call into question how we understand certain paradigms, such as the Hippocratic Oath in medicine. As a consequence, a reflection on the study of the risks associated with the ethical issues surrounding the design and manipulation of (...) this "massive data" seems to be essential. This book provides a direction and ethical value to these significant volumes of data. It proposes an ethical analysis model and recommendations to better keep this data in check. This empirical and ethico-technical approach brings together the first aspects of a moral framework directed toward thought, conscience and the responsibility of citizens concerned by the use of data of a personal nature. Defines Big Data applications in health Presents the ethical value of the medical datasphere via the description of a model of an ethical analysis of Big Data Provides the recommendations and steps necessary for successful management and governance of personal health data Helps readers determine what conditions are essential for the development of the study of Big Data. (shrink)

Background: Psychedelics are known for their powerful mental effects due to the activation of 5HT-2A receptors in the brain. During the 1950s and 1960s, research was conducted on these molecules until their criminalization. However, their clinical investigation as therapeutic tools for psychiatric disorders has revived the deontological ethics surrounding this subject. Questions arise as research on their therapeutic outcome becomes a reality. We aim to explore deontological ethics to understand the implications of psychedelics for the clinician, patient, and (...) society. Results: A total of 42 articles were considered for this review. Methods: A methodological search of psychedelic studies from 2017 to 2022 was conducted in PubMed, Scopus, EBSCOhost, and ScienceDirect to address the deontological ethics of clinical psychedelic use. Conclusion: Psychedelics need to be culturally contextualized, epistemic harm minimized and represented to ensure informed consent. Open data and commissions are needed to ensure safe and equal distribution. (shrink)

The use of smart mouthguards in contact sports like rugby aims to enhance player safety by providing real-time data on head impacts. These devices, equipped with sensors, measure collision force and frequency, potentially identifying concussions that might go unnoticed during gameplay. The idea is that such enhanced monitoring will enable teams, physicians and other stakeholders to better protect players from the effects of on-pitch injury through immediate detection of head trauma and the long-term provision of player data. While (...) we welcome the move towards a better understanding of the potential harm that contact sports athletes may experience, introducing a new avenue through which information about player performance and health can be collected brings several areas of ethical concern absent from traditional mouthguards. In this paper, we consider four noteworthy areas: device and data efficacy, player choice, the inherent issues of such data’s existence and the harmful consequences of this data’s usage and exposure. We argue that failing to identify and address the risks associated with smart mouthguards’ employment in the elite sporting sphere not only risks the mistreatment and misapplication of player data but may also jeopardise the acceptability of a novel health monitoring method that has the potential to prevent long-term debilitating diseases. (shrink)

As big data techniques become widespread in journalism, both as the subject of reporting and as newsgathering tools, the ethics of data science must inform and be informed by media ethics. This article explores emerging problems in ethical research using big data techniques. It does so using the duty-based framework advanced by W.D. Ross, who has significantly influenced both research science and media ethics. A successful framework must provide stability and flexibility. Without stability, ethical (...) precommitments will vanish as technology rapidly shifts costs. Without flexibility, traditional approaches will rapidly become obsolete in the face of technological change. The article concludes that Ross's duty-based approach both provides stability in the face of rapid technological change and flexibility to innovate to achieve the original purpose of basic ethical principles. (shrink)