(2013). Open Access Digital Data Sharing: Principles, Policies and Practices☆. Social Epistemology: Vol. 27, No. 1, pp. 47-67. doi: 10.1080/02691728.2012.760663.

ABSTRACT Data from digital technologies are increasingly integrated in public health research. In April of 2020, we interviewed a subset of participants who completed a survey approximately one month earlier. Using the survey, we contacted and interviewed participants who had expressed their willingness or unwillingness to share digital data for use in public health. We followed a directed content analysis approach for the analysis of the interview data. Among participants who had reported being unwilling to (...) share data, concerns about privacy, confidentiality, and the purpose of the research were cited. During the interviews, 76.9% of the participants who had who had previously indicated that they were unwilling to share their data, expressed willingness to share data in order to assist with COVID-19 prevention. Our results contribute to our understanding of people’s perspectives on sharing personal data and of the way their perspectives can vary as a function of potential uses of their personal information. (shrink)

The Digital Data Protection Act of 2023 marks a significant development in safeguarding user data privacy in India and simultaneously emerges as a critical instrument in the anti-trust domain. Recognizing the transformative impact of artificial intelligence on data processing, this paper explores the inadequacies of addressing anti-competitive data practices exclusively under the Competition Act. It highlights the Competition Commission of India’s acknowledgment of the challenges ‘Data Harvesting’ poses to competition law. The analysis extends beyond (...) conventional regulatory frameworks by arguing for an integrated approach considering the intersection of privacy and anti-trust issues. The paper advocates for responsible and mandatory data-sharing provisions under the Competition Act and segmented consent strategies under the Digital Data Protection Act. This dual approach addresses the legal complexities of rapid technological advancements, urging a cohesive policy response that spans multiple regulatory perspectives. (shrink)

Many actors mobilize the cognitive, legal and technical tool-box of data protection when they discuss and address controversial issues such as digital mass surveillance. Yet, critical approaches to the digital only barely explore the politics of data protection in relation to data-driven governance. Building on governmentality studies and Actor-Network-Theory, this article analyses the potential and limits of using data protection to critique the ‘digital age’. Using the conceptual tool of dispositifs, it sketches an (...) analytics of data protection and the emergence of its configuration as ‘data protection by design and by default’. This exploration reminds us that governing through data implies, first and foremost, governing digital data. (shrink)

In recent years, critical scholarship has drawn attention to increasing power differentials between corporations that use data and people whose data is used. A growing number of scholars see digital data and information commons as a way to counteract this asymmetry. In this paper I raise two concerns with this argument: First, because digital data and information can be in more than one place at once, governance models for physical common-pool resources cannot be easily (...) transposed to digital commons. Second, not all data and information commons are suitable to address power differentials. In order to create digital commons that effectively address power asymmetries we must pay more systematic attention to the issue of exclusion from digital data and information commons. Why and how digital data and information commons exclude, and what the consequences of such exclusion are, decide whether commons can change power asymmetries or whether they are more likely to perpetuate them. (shrink)

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

The proliferation of digital data and internet-based research technologies is transforming the research landscape, and researchers and research ethics communities are struggling to respond to the ethical issues being raised. This paper discusses the findings from a collaborative project that explored emerging ethical issues associated with the expanding use of digital data for research. The project involved consulting with researchers from a broad range of disciplinary fields. These discussions identified five key sets of issues and informed (...) the development of guidelines orientated to meet the needs of researchers and ethics committee members. We argue that establishing common approaches to assessing ethical risks of research involving digital data will promote consistency in the ethical standards for research, enable the smooth functioning of ethics committees, and sustain public confidence in research. We conclude with recommendations for the development of educational resources for ethics committees, data management guidelines and further public education. (shrink)

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data (...) production and circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

Transhumanism : in a nutshell -- On a silicon-based transhumanism -- On a carbon-based transhumanism -- A fictive ethics -- The end of the beginning.

The AI and Society discourse has previously drawn attention to the ways that digital systems embody the values of the technology development community from which they emerge through the development and deployment process. Research shows how this effect leads to a particular treatment of gender in computer systems development, a treatment which lags far behind the rich understanding of gender that social studies scholarship reveals and people across society experience. Many people do not relate to the narrow binary gender (...) options of male or female, and many people express their gender identity in much richer ways than the sex/gender binary female/woman and male/man Boolean terms will allow. We ask: are “born-digital” gendered datasets in digital systems experienced as marginalising by those who express their identity beyond the male/female binary? Case Study: Ireland. To answer this universal question, this paper presents the findings of an empirical case study of people in Ireland with diverse gender identities and expressions, and their experiences with public data systems and new technologies. In spite of great social changes in Ireland which have led to constitutional change in favour of LGBTQI + people, born-digital systems were experienced by respondents as embodying socio-cultural values which were no longer accepted in society at large. For many of the respondents, digital technologies routinely marginalise them in all kinds of ways. These systems keep alive violence and oppression long after civil rights have been enshrined in constitutional law. This study is just one example of the way assumptions about digital are disengaged from society-at-large. It is a call to arms to all who are passionate about socially-responsible technology. (shrink)

The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...) results of the main empirical research carried out in recent years, in particular those conducted with a promising methodology: Digital Trace Data Analysis. (shrink)

Digital data is an increasing and continual presence across the sites, activities and relationships of everyday life. In this article we explore what data presence means for the ways that the everyday is organised, sensed, and anticipated. While digital data studies have demonstrated how data is deeply entangled with the way in which everyday life is lived out and valued, at the same time our relationships with data are riddled with anxieties or small (...) niggles or tricky trade-offs and their use is often chaotic and muddled, part of the inevitable uncertainty about what will happen next. If the presence of data is part of the environments we inhabit, this raises the question of how and why data is valuable to us and what forms of hope and trust enable this value to further develop. (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)

This article contrasts two reform proposals articulated in recent debates about how to democratize the digital economy: data-owning democracy and digital socialism. A data-owning democracy is a political-economic regime characterized by the widespread distribution of data as capital among citizens, whereas digital socialism entails the social ownership of productive assets in the digital economy and popular control over digital services. The article argues that while a degree of complementarity exists between the two, (...) there are important limitations to theories of data-owning democracy that have not yet received significant attention within the literature. The bulk of the article highlights three ways in which digital socialists would consider a data-owning democracy to fall short of achieving a more just digital economy: a lack of workplace democracy, limitations in terms of scope, and a lack of democratic control over long-term investment decisions in new technology. The article thus contributes to determining what is at stake in recent debates about how to democratize the digital economy. (shrink)

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for (...) ethical, anti-oppressive digital data-driven research in the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data. (shrink)

Digital data inundation has far-reaching implications for: disciplinary jurisdiction; the relationship between the academy, commerce and the state; and the very nature of the sociological imagination. Hitherto much of the discussion about these matters has tended to focus on ‘transactional’ data held within large and complex commercial and government databases. This emphasis has been quite understandable – such transactional data does indeed form a crucial part of the informational infrastructures that are now emerging. However, in recent (...) years new sources of data have become available that possess a rather different character. This is data generated in the cultural sphere, not only as a result of routine transactions with various digital media but also as a result of what some would want to view as a shift towards popular cultural forms dominated by processes of what has been termed prosumption. Our analytic focus here is on contemporary prosumption practices, digital technologies, the public life of data and the playful vitality of many of the ‘glossy topics’ that constitute contemporary popular culture. (shrink)

This chapter works to relativize the existential fears promoted by classic transhumanism concerning the imminent rise of superintelligence. It clarifies the distinction between weak AI, strong AI (AGI), and superintelligence, arguing that silicon-based entities show no current indication of possessing intentionality or digital consciousness. The true ethical urgency is directed toward the access and exploitation of digital data. Analysing projects like Musk’s Starlink and Neuralink reveals how the global conflict over digital data (‘Bytes are Barrels’) (...) is actively altering power dynamics. Furthermore, the chapter sharply separates innovation (AI’s capacity for generating new outputs) from true creativity (the human selection, refinement, and application of intentionality). (shrink)

This commentary is an attempt to begin to identify and think through some of the ways in which sociocultural theory may contribute to understandings of the relationship between humans and digital data. I develop an argument that rests largely on the work of two scholars in the field of science and technology studies: Donna Haraway and Annemarie Mol. Both authors emphasised materiality and multiple ontologies in their writing. I argue that these concepts have much to offer critical (...) class='Hi'>data studies. I employ the tropes of companion species, drawn from Haraway, and eating data, from Mol, and demonstrate how these may be employed to theorise digital data–human assemblages. (shrink)

This article develops and mobilises the concept of ‘mundane data’ as an analytical entry point for understanding Big Data. We call for in-depth investigation of the human experiences, routines, improvisations and accomplishments which implicate digital data in the flow of the everyday. We demonstrate the value of this approach through a discussion of our ethnographic research with self-tracking cycling commuters. We argue that such investigations are crucial in informing our understandings of how digital data (...) become meaningful in mundane contexts of everyday life for two reasons: first because there is a gap in our understanding of the contingencies and specificities through which big digital data sets are produced, and second because designers and policy makers often seek to make interventions for change in everyday contexts through the presentation of mundane data to consumers but with little understanding of how people produce, experience and engage with these data. (shrink)

One facet of Stefan Lorenz Sorgner’s scholarship is immediately visible in his diligent academic production: the 21st Century is a leading spatio-temporal unit of his analyses. Although such assessment could be considered a rough generalization, it should not be taken for granted. To be placed in the contextual core of the current epoch is of particular relevance with respect to multiple academic trajectories Sorgner navigates and is almost always an achievement rather than an expected, ready-made content. Namely, more often than (...) not, especially in authorship with transhumanist labels, the temporal and conceptual curves are not subtly controlled and fine-tuned into a well-balanced thinking product that contributes to facing the current and potential issues of the relatively near and projectively feasible futures. Such gross asymmetry has various dangerous consequences Sorgner unequivocally warns about, opting deliberately to be an active chronicler of the ‘history of the present’ instead. This approach entails the dismissal of ‘guaranteed’ futurity vectors, be it Christian linearity or Kurzweilian exponentiality, but no less of petrified presentism or conventional historical layers of any inherited anthropology and the specific ethical account it is built upon. (shrink)

This article extends property-owning democracy to the digital realm and introduces “data-owning democracy,” a new political economic regime characterized by the wide distribution of data as capital among citizens. Drawing on republican theory and acknowledging data's unique role in the digital economy, it proposes a two-tier model that combines different modes of data ownership and corresponding rights. The first layer of “data-owning democracy” is characterized by a digital public infrastructure that enables citizens (...) to collectively generate data and have a say in how their citizen data are used. In the second layer, individuals automatically receive machine-readable copies of their data whenever they are generated—a slightly more advanced form of the European Union's existing right to data portability (Art. 20). With its focus on empowerment, data-owning democracy is designed to be complementary to existing data protection regulations. It also illustrates how political theory more broadly, and republican theory specifically, can be instructive for specifying the normative components of a new political economy dealing with questions of empowerment and digital rights. (shrink)

The increasing availability of digital data reflecting economic and human development, and in particular the availability of data emitted as a by-product of people’s use of technological devices and services, has both political and practical implications for the way people are seen and treated by the state and by the private sector. Yet the data revolution is so far primarily a technical one: the power of data to sort, categorise and intervene has not yet been (...) explicitly connected to a social justice agenda by the agencies and authorities involved. Meanwhile, although data-driven discrimination is advancing at a similar pace to data processing technologies, awareness and mechanisms for combating it are not. This paper posits that just as an idea of justice is needed in order to establish the rule of law, an idea of data justice – fairness in the way people are made visible, represented and treated as a result of their production of digital data – is necessary to determine ethical paths through a datafying world. Bringing together the emerging scholarly perspectives on this topic, I propose three pillars as the basis of a notion of international data justice: visibility, engagement with technology and antidiscrimination. These pillars integrate positive with negative rights and freedoms, and by doing so challenge both the basis of current data protection regulations and the growing assumption that being visible through the data we emit is part of the contemporary social contract. (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.

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka (...) tool, is the analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

Wearable self-tracking devices capture multidimensional health data and offer several advantages including new ways of facilitating research. However, they also create a conflict between individual interests of avoiding privacy harms, and collective interests of assembling and using large health data sets for public benefits. While some scholars argue for transparency and accountability mechanisms to resolve this conflict, an average user is not adequately equipped to access and process information relating to the consequences of consenting to further uses of (...) her data. As an alternative, this paper argues for fiduciary relationships, which put deliberative demands on digital health data controllers to keep the interests of their data subjects at the forefront as well as cater to the contextual nature of privacy. These deliberative requirements ensure that users can engage in collective participation and share their health data at a lower risk of privacy harms. This paper also proposes a way to balance the flexible and open-ended nature of fiduciary law with the specific nature and scope of fiduciary duties that digital health data controllers should owe to their data subjects. (shrink)

Digital personal data is increasingly framed as the basis of contemporary economies, representing an important new asset class. Control over these data assets seems to explain the emergence and dominance of so-called “Big Tech” firms, consisting of Apple, Microsoft, Amazon, Google/alphabet, and Facebook. These US-based firms are some of the largest in the world by market capitalization, a position that they retain despite growing policy and public condemnation—or “techlash”—of their market power based on their monopolistic control of (...) personal data. We analyse the transformation of personal data into an asset in order to explore how personal data is accounted for, governed, and valued by Big Tech firms and other political-economic actors. However, our findings show that Big Tech firms turn “users” and “user engagement” into assets through the performative measurement, governance, and valuation of user metrics, rather than extending ownership and control rights over personal data per se. We conceptualize this strategy as a form of “techcraft” to center attention on the means and mechanisms that Big Tech firms deploy to make users and user data measurable and legible as future revenue streams. (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)

The rise of big digital data is changing the framework within which linguists, sociologists, anthropologists, and other researchers are working. Semiotics is not spared by this paradigm shift. A data-driven computational semiotics is the study with an intensive use of computational methods of patterns in human-created contents related to semiotic phenomena. One of the most promising frameworks in this research program is the Semantic Vector Space (SVS) models and their methods. The objective of this article is to (...) contribute to the exploration of the SVS for a computational semiotics by showing what types of semiotic analysis can be accomplished within this framework. The study is applied to a unique body of digitized artworks. We conducted three short experiments in which we explore three types of semiotic analysis: paradigmatic analysis, componential analysis, and topic modelling analysis. The results reported show that the SVS constitutes a powerful framework within which various types of semiotic analysis can be carried out. (shrink)

This essay explores the return of the subject in the computational context, which I address as a digital subject. This digital subject encompasses a digital identifier, correlations in data or a data profile, moving between biological characteristics and symbolic expression. I focus on the processes through which digital subjects are constructed by matching, correlating, modelling, as well as how they become enactive. The ways of pulling data together into a digital subject is (...) often presented as a logic of fact, where data is equated with documentary evidence. Instead, I propose the notion of the distance in which digital subjects are produced. Indexicality comes from outside of data, whereas the regard for the thick distance becomes a mark of the form of knowledge. I conclude by arguing for a posthumanities approach that establishes the distance while allowing for different subjects to be called upon. (shrink)

Self-reported data are regarded by medical researchers as invalid and less reliable than data produced by experts in clinical settings, yet individuals can increasingly contribute personal information to medical research through a variety of online platforms. In this article we examine this ‘participatory turn’ in healthcare research, which claims to challenge conventional delineations of what is valid and reliable for medical practice, by using aggregated self-reported experiences from patients and ‘pre-patients’ via the internet. We focus on 23andMe, a (...) genetic testing company that collects genetic material and self-reported information about disease from its customers. Integral to this research method are relations of trust embedded in the information exchange: trust in customers’ data; trust between researchers/company and research subjects; trust in genetics; trust in the machine. We examine the performative dimension of these trust relations, drawing on Shapin and Schaffer’s (1985) discussion of how material, literary and social technologies are used in research in order to establish trust. Our scepticism of the company’s motives for building trust with the self-reporting consumer forces us to consider our own motives. How does the use of customer data for research purposes by 23andMe differ from the research practices of social scientists, especially those who also study digital traces? By interrogating the use of self-reported data in the genetic testing context, we examine our ethical responsibilities in studying the digital selves of others using internet methods. How researchers trust data, how participants trust researchers, and how technologies are trusted are all important considerations in studying the social life of digital data. (shrink)

The research aims to investigate the characteristics and risks associated with the handling of sensitive data in the implementation of public policies within the digital government model. To achieve this, a hypothetical-deductive method and bibliographic and documentary procedures are employed, proposing an interdisciplinary discussion on technological advancement, data protection, transparency, and public policies. The justification revolves around the importance of a comprehensive, cohesive system that genuinely protects sensitive personal data, considering the need to keep pace with (...) technological developments and maintain a minimum level of security in the digital age at both national and international levels. There is no doubt that the judicious and strategic use of data has the potential to drive substantial improvements in policy evaluation, decision-making, and the relationship between government and citizens. However, dealing with personal data also means addressing risks with responsibility, transparency, and respect for individual rights. (shrink)

This article explores the philosophical parallels between Plato’s theory of Perfect Forms and the modern concept of digital twins—virtual replicas of physical objects, systems, or processes. In Plato’s philosophy, the physical world is seen as an imperfect reflection of a higher realm of ideal, immutable Forms. Similarly, digital twins offer a dynamic, data-driven representation that often surpasses the accuracy and functionality of their physical counterparts. By continuously updating with real-time information, digital twins aim to create a (...) “more perfect” version of reality, challenging our traditional understanding of physical and virtual worlds. This article investigates whether digital twins achieve Plato’s vision of idealized perfection or represent a new, evolving form of idealism in the digital age, raising important questions about the nature of reality, truth, and the ongoing pursuit of optimization in both the physical and virtual realms. (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)

A recent report from the UN makes the case for “global data literacy” in order to realise the opportunities afforded by the “data revolution”. Here and in many other contexts, data literacy is characterised in terms of a combination of numerical, statistical and technical capacities. In this article, we argue for an expansion of the concept to include not just competencies in reading and working with datasets but also the ability to account for, intervene around and participate (...) in the wider socio-technical infrastructures through which data is created, stored and analysed – which we call “data infrastructure literacy”. We illustrate this notion with examples of “inventive data practice” from previous and ongoing research on open data, online platforms, data journalism and data activism. Drawing on these perspectives, we argue that data literacy initiatives might cultivate sensibilities not only for data science but also for data sociology, data politics as well as wider public engagement with digital data infrastructures. The proposed notion of data infrastructure literacy is intended to make space for collective inquiry, experimentation, imagination and intervention around data in educational programmes and beyond, including how data infrastructures can be challenged, contested, reshaped and repurposed to align with interests and publics other than those originally intended. (shrink)

In this article, we introduce and demonstrate the concept-metaphor of broken data. In doing so, we advance critical discussions of digital data by accounting for how data might be in processes of decay, making, repair, re-making and growth, which are inextricable from the ongoing forms of creativity that stem from everyday contingencies and improvisatory human activity. We build and demonstrate our argument through three examples drawn from mundane everyday activity: the incompleteness, inaccuracy and dispersed nature of (...) personal self-tracking data; the data cleaning and repair processes of Big Data analysis and how data can turn into noise and vice versa when they are transduced into sound within practices of music production and sound art. This, we argue is a necessary step for considering the meaning and implications of data as it is increasingly mobilised in ways that impact society and our everyday worlds. (shrink)

PurposeThis paper aims to discuss characteristics of Korea’s system responses with a research framework of the structure, conduct and performance theory and explain the role of information, communication technologies (ICT) and Big Data from a technology-mediated control (TMC) perspective.Design/methodology/approachThis study examines the contextual nature of Korea’s diagnostic, preventive and treatment efforts. Particular attention is paid to issues related to the effective use of Big Data analytics and its applications, reporting mechanisms and public safety measures. The research model defines (...) key factors in assessing the effectiveness of Korea’s responses.FindingsFindings of this research suggest: effective strategic planning and operational execution use well-tested and designed crisis-responsive manuals; linkage role of ICT/Big Data is prominent in trace, test and treat and participation (3T + P); and aggressive epidemic investigations require synergistic efforts of national and local government units, broad societal support and participation and contribution of global firms offering their domestic and global supply chain network capabilities.Research limitations/implicationsThe Korean Government's effective response experiences suggest the synergy of political, social, cultural and technological factors. Future studies may explore how personal privacy and public safety are both achieved in different social–cultural–political contexts (Ahnet al., 2020; Delgadoet al., 2020; Sharma and Bashir, 2020). Other emerging organizational issues and international comparative studies are worth further investigation in future studies.Practical implicationsThis case study suggests how to apply ICT capabilities for organizing a national response to the coronavirus pandemic (COVID-19) pandemic. Public and private partnership in the framework of sociotechnological synergy (i.e. integration of ICT and social orchestration) is essential for the 3T process. In support of public policy initiatives, global firms share their IT infrastructure and supply chain integration experiences to accommodate global-level crises like the COVID-19 pandemic.Social implicationsThis study extends the TMC framework to a national level. In the adapted TMC framework, the control source, control target and linkage mechanism are specified. Using TMC, this shows the dynamic roles of ICT/Big Data in Korea’s COVID-19 response experiences.Originality/valueThe impacts of the COVID-19 are rapid and enormous. Despite the controversial early policy decisions and the rapid rise of confirmed patients, the world has recognized Korea’s effective responses to the COVID-19 pandemic. (shrink)

Large corporations are attracting criticism for their quasi-monopolist role in the digital data domain. It has been argued that they are no longer regular market participants but have become de facto market regulators against whom public and civil society actors are powerless even when faced with stark ethical misconduct. Companies such as Google, Amazon, Facebook, and Apple (GAFA) have become a new Leviathan: a monster for which people give up freedoms in exchange for other goods that they consider (...) essential. Data donation is a strategy that could, if certain conditions are met, help tackle the overarching power of multinational enterprises. I will propose that data donation, understood as a specific type of transaction, has three distinctive characteristics: relationality, indirect reciprocity and multiplicity. I suggest ways in which ethical and regulatory frameworks for data donation should consider these characteristics to ensure that data donations respond to the institutional and power relationships that digital data use is embedded in, that data donations contribute to the public good, and that they and protect the personal needs and interests of people involved in it. (shrink)

Digital ethics is increasingly applied in practice within contexts such as public service and corporate environments. However, a significant challenge remains in understanding how to implement these principles effectively without diluting their efficacy and meaningful impact. While organisations employ a range of solutions, such as committees, training, audits, or tools to bridge the often cited “principles to practice gap,” there is a recognised need to tailor these solutions to address domain-relevant challenges, such as in data analytics. Despite the (...) substantial role of data analytics in driving decision-making processes and fostering innovation across various sectors, there is a notable gap in research focused on how digital ethics is operationalised in data analytics. Our work presented here addresses this gap by discussing how operationalisation of digital ethics can be achieved within this domain. The challenges and insights from the Analytics Center of Excellence at Merck KGaA, a multinational science and technology company, are utilised as a case study for implementing digital ethics within this domain. We present the development and deployment of Merck’s Digital Ethics Check (MDEC), an instrument tailored to address the ethical challenges in data analytics. The present work underscores the advantages of a semi-automated integration of digital ethics into data analytics. In doing so, we highlight the relevance of the challenges encountered in the practical application of digital ethics within the domain of data analytics in a business context. Our work provides a practical impetus to guide organisations seeking to embed ethical considerations within their data analytics operations. (shrink)

Background The growing diffusion of artificial intelligence, data science and digital health has highlighted the role of collection of data and biological samples, thus raising legal and ethical concerns regarding its use and dissemination. Further, the expansion of biobanking, from the basic collection of frozen specimens to the virtual biobanks of specimens and associated data that exist today, has given a revolutionary potential on healthcare systems, particularly in the field of neurological diseases, due to the inaccessibility (...) of central nervous system and the need of non-invasive investigation approaches. Informed Consent (IC) is considered mandatory in all research studies and specimen collections, and must specifically take into account the ethical respect to the individuals to whom the used biological material and data belong. Methods We evaluated the attitudes of patients with neurological diseases (NP) and healthy volunteers (HV) towards the donation of biological samples to a biobank for future research studies on neurological diseases, and limitations on the use of data, related to the requirements set by the General Data Protection Regulation (GDPR). The study involved a total of 1454 subjects, including 502 HVs and 952 NPs, recruited at Santa Lucia Foundation IRCCS, Rome, from 2020 to 2024. Results We found that (i) almost all subjects agreed with the participation in biobanking (ii) and authorization to genetic studies (HV = 99.1%; NP = 98.3%); Regarding the return of results, (iii) we found a statistically significant difference between NP and HV, the latter preferring not to be informed of potential results (HV = 43%; NP = 11.3%; p < 0.0001); (iv) a small number limited the sharing inside European Union (EU) (HV = 4.6%; NP = 6.6%), whereas patients were more likely to refuse transfer outside EU (HV = 7.4%; NP = 10.7% p = 0.05); (v) nearly all patients agreed with the use of additional health data from EMR for research purposes (98.9%). Conclusions Consent for the donation of material for research purposes is crucial for biobanking and biomedical research studies that use biological material of human origin. Here, we have shown that choices regarding participation in a neurological biobank can be different between HVs and NPs, even if the benefit for research and scientific progress is recognized. NP have a strong interest in being informed of possible results but limit sharing of samples, highlighting a perception of greater individual or relative benefit, while HV prefer a wide dissemination and sharing of data but not to have the return of the results, favoring a possible benefit for society and knowledge. The results underline the need to carefully manage biological material and data collected in biobanks, in compliance with the GDPR and the specific requests of donors. (shrink)

This systematic review aims to synthesize the current knowledge about the applications and challenges of Artificial Intelligence (AI) technologies in healthcare, while evaluating the extent to which the European Union (EU) AI Act and the European Health Data Space (EHDS) contribute to ensuring responsible, secure, and ethically sound adoption of AI in clinical practice. This review adheres to the guidelines set by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and has also been registered in PROSPERO. The (...) PubMed®, Web of Science™, Scopus and ScienceDirect® databases were used as scientific search strategy. In addition, records identified through other sources (grey literature) were also assessed for eligibility and included. All studies published between 2020 and 2024 about the application of AI and its regulation and ethical implications, particularly in healthcare, were included. Eligible studies were assessed for potential risk of bias during data extraction and quality evaluation screening. A total of 76 studies were included. Although AI technologies have several applications in the healthcare sector such as disease diagnosis, treatment, clinical data management, automated surgery, remote health monitoring, elderly patient care and/ or biomedical research, important ethical issues are raised when using AI, namely data privacy, safety, lack of transparency, explainability, trust and potential biases. A proper application and compliance with established ethical principles, and legal regulations such as the EU AI Act and the EHDS are fundamental to ensure a responsible, safe, sustainable and trustworthy use of AI in healthcare. (shrink)

This paper explores the political potential of digital failure as a refusal to work in service of today’s dataveillance society. Moving beyond criticisms of flawed digital systems, this paper traces the moments of digital failure that seek to break, rather than fix, existing systems. Instead, digital failure is characterized by pesky data that sneaks through the cracks of digital capitalism and dissipates into the unproductive; it supports run-away data prone to misidentifications by (...) class='Hi'>digital marketers, coders, and content moderators; and it celebrates data predisposed to “back-talk” with playful irreverence toward those that seek to bring order through normative categorization and moderation. I call these data entropic, fugitive, and queer and explore their mischievous practices through three case studies: the unaccountable data in identity resolution, public shaming of the ImageNet training data, and reading practices of sex worker and influencer, @Charlieshe. Together these case studies articulate the political potential of digital failure as a process of unbecoming the good data subject by pushing past the margins of legibility, knowability, and thinkability, to reveal what is made illegible, unknowable, and unthinkable to data’s seeing eye. As predictive analytics, automated decision-systems, and artificial intelligence take on increasingly central roles in public governance, digital failure reveals how data itself is a flawed concept prone to political abuse and social engineering to protect the interests of the powerful, while keeping those marginalized over-surveilled and underrepresented. (shrink)

The rapid adoption of digital technologies has revolutionized business operations and introduced emerging concepts such as Digital Twin (DT) technology, which has the potential to predict system responses before they occur, making it an attractive option for smart and sustainable tourism. However, implementing DT software systems poses significant challenges, including compliance with regulations and effective communication among stakeholders, and concerns surrounding security, privacy, and trust with the use of big data. To address these challenges, this paper proposes (...) a documentation framework for architectural decisions (DFAD) that applies the concept of big data governance to the digital system. The framework aims to ensure accountability, transparency, and trustworthiness while adhering to rules and regulations. To demonstrate its applicability, a case study and three case scenarios on the potential use of Mobile Positioning Data (MPD) in Indonesia for DT technology in smart and sustainable tourism were examined. The paper highlights the benefits of DFAD in shaping stakeholder communication and human–machine interactions while leveraging the potential of MPD to measure tourism statistics by Statistics Indonesia since 2016. Not only the documentation framework promotes compliance with regulations, but it also facilitates effective communication among stakeholders and enhances trust and transparency in the use of big data in DT technology for smart and sustainable tourism. This paper emphasizes the importance of effective big data governance and its potential to promote sustainable tourism practices. The multidisciplinarity approach on political science, software engineering, tourism, and official statistics provides an opportunity for academic contribution and decision-making processes. (shrink)

This paper explores the embedding of data producing technologies in people's everyday lives and practices. It traces how repeated encounters with digital data operate to naturalise these entities, while often blindsiding their agentive properties and the ways they get implicated in processes of exploitation and governance. I propose and develop the notion of ‘data doxa’ to conceptualise the way in which digital data – and the devices and platforms that stage data – have (...) come to be perceived in Western societies as normal, necessary and enabling. The ‘data doxa’ concept also accentuates the enculturation of many individuals into a data sharing habitus which frames digital technologies in simplistic terms as panaceas for the problems associated with contemporary life, figures of progress and convenience, and mediums of knowledge, pleasure and identity. I suggest that three types of data-based relations contribute to the formation of this doxic sensibility: fetishisation, habit and enchantment. Each of these relations come to mediate public understandings of digital devices and the data they generate, obscuring the multifaceted nature and hidden depths of data and their propensity to double up as technologies of exposure and discipline. As a result of this situation, imaginative educational programs and revamped regulatory frameworks are urgently needed to inform individuals about the contribution of data to the leveraging of value and power in today's digital economies, but also to protect them from experiencing data-based harms. (shrink)

Advances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need-availability gap by increasing resources for mental health care. One resource considered especially promising is smartphone psychotherapy chatbots, i.e., artificially intelligent bots that offer cognitive behavior therapy to their users with the aim of helping them improve their mental health. While a number of studies have highlighted the positive outcomes of using smartphone psychotherapy (...) chatbots to handle various anxiety related problems no conclusive data illustrate their effectiveness or warrant their use in mental illness diagnosis and treatment settings. Yet smartphone psychotherapy is highly endorsed by experts in the field of mental health research. In this paper, I focus on the specific features of smartphone psychotherapy chatbots intended for the diagnosis and treatment of mental illness and criticize three popular promises; i.e., they enable early diagnosis and intervention through digital phenotyping; they defy the stigma of mental illness diagnosis and treatment; they offer increased access to mental health treatment globally. Going against the popular enthusiasm, I argue smartphone psychotherapy chatbots have epistemic and ethical limitations in the diagnosis and treatment of illnesses. In light of these, I encourage researchers, clinicians, policy makers, patients, and caregivers to pause before jumping on the artificial intelligence bandwagon to seek solutions for mental illness on the grounds of these three promises. (shrink)

Data hiding and watermarking are considered one of the most important topics in cyber security. This article proposes an optimized method for embedding a watermark image in a cover medium (color image). First, the color of the image is separated into three components (RGB). Consequently, the discrete wavelet transform is applied to each component to obtain four bands (high–high, high–low, low–high, and low–low), resulting in 12 bands in total. By omitting the low–low band from each component, a new square (...) matrix is formed from the rest bands to be used for the hiding process after adding keys to it. These keys are generated using a hybrid approach, combining two chaotic functions, namely Gaussian and exponential maps. The embedding matrix is divided into square blocks with a specific length, each of which is converted using Hessenberg transform into two matrices, P and H. For each block, a certain location within the H-matrix is used for embedding a secret value; the updated blocks are assembled, and the reverse process is performed. An optimization method is applied, through the application of the firework algorithm, on the set of the initial values that generate keys. Using an optimization procedure to obtain keys requires performing lowest possible change rate in an image and maintain the quality of the image. To analyze and test the efficiency of the proposed method, mean-square error (MSE) and peak signal-to-noise ratio (PSNR) measurements are calculated. Furthermore, the robustness of the watermark is computed by applying several attacks. The experimental results show that the value of the MSE is reduced by about 0.01 while the value of the PSNR is increased by about 1.25 on average. Moreover, the proposed method achieved a high-retrieval rate in comparison with the non-optimization approach. (shrink)