With the surge in the number of data and datafied governance initiatives, arrangements, and practices across the globe, understanding various types of such initiatives, arrangements, and their structural causes has become a daunting task for scholars, policy makers, and the public. This complexity additionally generates substantial difficulties in considering different data(fied) governances commensurable with each other. To advance the discussion, this study argues that existing scholarship is inclined to embrace an organization-centric perspective that primarily concerns factors and (...) dynamics regarding data and datafication at the organizational level at the expense of macro-level social, political, and cultural factors of both data and governance. To explicate the macro, societal dimension of data governance, this study then suggests the term “social data governance” to bring forth the consideration that data governance not only reflects the society from which it emerges but also (re)produces the policies and practices of the society in question. Drawing on theories of political science and public management, a model of social data governance is proposed to elucidate the ideological and conceptual groundings of various modes of governance from a comparative perspective. This preliminary model, consisting of a two-dimensional continuum, state intervention and societal autonomy for the one, and national cultures for the other, accounts for variations in social data governance across societies as a complementary way of conceptualizing and categorizing data governance beyond the European standpoint. Finally, we conduct an extreme case study of governing digital contact-tracing techniques during the pandemic to exemplify the explanatory power of the proposed model of social data governance. (shrink)

Digital Earth applications provide a common ground for visualizing, simulating, and modeling real-world situations. The potential of Digital Earth applications has increased significantly with the evolution of artificial intelligence systems and the capacity to collect and process complex amounts of geospatial data. Yet, the widespread techno-optimism at the root of Digital Earth must now confront concerns over high-risk artificial intelligence systems and power asymmetries of a datafied society. In this commentary, we claim that not only can current debates about (...) data governance and ethical artificial intelligence inform development in the field of Digital Earth, but that the specificities of geospatial data, together with the expectations surrounding Digital Earth applications, offer a fruitful lens through which to examine current debates on data governance and artificial intelligence ethics. In particular, we argue that for the implementation of ethical artificial intelligence and inclusive approaches to data governance, Digital Earth initiatives need to involve stakeholders and communities at the local level and be sensitive to social, legal, cultural, and institutional contexts, including conflicts that might arise within those contexts. (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 financial applications of data technology have enabled the rise of Chinese fintech industries. As part of people's everyday lives, fintech apps have helped companies collect vast amounts of user data for business profit and social good. This paper takes an open-systems approach to study the constructs of this emerging idea of data governance, particularly its operational logic, involved stakeholders, and socio-cultural consequences in the context of fintech industries in China. It asserts that data (...) class='Hi'>governance at the company level has been realized through three types of tasks: standardization, configuration, and monetization of big data. These tasks are oriented by the imperative of business innovation which is considered pivotal for the company to survive the competition. An ensemble of internal and external actors joined the governing process, but internal actors hold the stake. The goal of innovation drives the companies to speedily collect data from a wider variety of sources, thus having to establish more sophisticated systems to manage and utilize those data. These complicated systems, however, have urged the government to strengthen regulatory controls alongside the general support of business innovation. This study unveils the performative aspects of big-data-based innovation at fintech firms. It also helps to understand the pathology of the innovation-governance paradox shared in the data economy in the global context. (shrink)

The article examines four models of data governance emerging in the current platform society. While major attention is currently given to the dominant model of corporate platforms collecting and economically exploiting massive amounts of personal data, other actors, such as small businesses, public bodies and civic society, take also part in data governance. The article sheds light on four models emerging from the practices of these actors: data sharing pools, data cooperatives, public (...) class='Hi'>data trusts and personal data sovereignty. We propose a social science-informed conceptualisation of data governance. Drawing from the notion of data infrastructure we identify the models as a function of the stakeholders’ roles, their interrelationships, articulations of value, and governance principles. Addressing the politics of data, we considered the actors’ competitive struggles for governing data. This conceptualisation brings to the forefront the power relations and multifaceted economic and social interactions within data governance models emerging in an environment mainly dominated by corporate actors. These models highlight that civic society and public bodies are key actors for democratising data governance and redistributing value produced through data. Through the discussion of the models, their underpinning principles and limitations, the article wishes to inform future investigations of socio-technical imaginaries for the governance of data, particularly now that the policy debate around data governance is very active in Europe. (shrink)

Background The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. Objectives We aimed to identify concerns and recommendations regarding how to use big data on (...) opioid use in ethical ways. Methods We conducted focus groups and interviews in 2019 with 39 big data stakeholders who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. Results Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide, enact shared data governance, cultivate public trust and earn social license for big data uses, and refocus ethical approaches. Conclusions Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. (shrink)

The storage and access of genetic testing results have unique considerations for medical records. Initially, genetic testing was limited to patients with single gene diseases. Genetic medicine and testing have expanded, as have concerns about appropriately handling genetic information. In this study, we surveyed the management of genetic information in general hospitals in Japan using a questionnaire on access restrictions. Our questions included whether any other medical information was managed in a unique way. We identified 1037 hospitals designated for clinical (...) training located throughout Japan and received responses from 258 hospitals, and 191 reported that they handle genetic information and results of genetic tests. Of the 191 hospitals that handle genetic information, 112 hospitals implement access restrictions to genetic information. Seventy-one hospitals, one of which uses paper medical records rather than electrical medical records, do not enforce access restrictions. For eight hospitals, it was not known whether access restrictions were enforced or not. The responses from these hospitals indicated that access restrictions and storage methods varied across institution type (e.g., general vs. university hospitals), institution size, and the presence of a clinical genetics department. Other information, such as infectious disease diagnosis, psychological counseling records, abuse, and criminal history, was also subject to access restriction in 42 hospitals. The disparity in how medical facilities handle sensitive genetic information demonstrates a need for discussion between medical professionals and the general public on the storage of sensitive records, including genetic information. (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)

Advances in neuroscience and other disciplines are producing large-scale brain data consisting of datasets from multiple organisms, disciplines, and jurisdictions in different formats. However, due to the lack of an international data governance framework brain data is currently being produced under various contextual ethical and legal principles which may influence key stakeholders involved in the generation, collection, processing and sharing of brain data thereby raising ethical and legal challenges. In addition, despite the demand for a (...) brain data governance framework that accounts for culture, there is a gap in empirical research and actions to understand how key stakeholders around the world view these issues using neuroscientists who are affected by these ethical and legal principles. Therefore, using the research question how do ethical and legal principles influence data governance in neuroscience? we attempt to understand the perceptions of key actors on the principles, issues and concerns that can arise from brain data research. We carried out interviews with 21 leading international neuroscientists. The analytical insights revealed key ethical and legal principles, areas of convergence, visibility, and the contextual issues and concerns that arise in brain data research around these principles. These issues and concerns circulate around intimately connected areas which include ethics, human rights, regulations, policies and guidelines, and participatory governance. Also, key contextual insights around animal research and ethics were identified. The research identifies key principles, issues, and concerns that need to be addressed in advancing the development of a framework for global brain data governance. By presenting contextual insights from neuroscientists across regions, the study contributes to informing discussions and shaping policies aimed at promoting responsible and ethical practices in brain data research. The research answers the call for a cross cultural study of global brain data governance and the results of the study will assist in understanding the issues and concerns that arise in brain data governance. (shrink)

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance (...) approach honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management. (shrink)

In recent years, universities have been urged to restructure and re-evaluate their ability to trace and monitor their students as the “smart campus” is being built upon datafication, while networked apps and sensors serve as the means through which its constituents are connected and governed. This paper advances a dialectical and communication-centered approach to the Internet of Things campus ecosystem and provides an empirical investigation into the tensions experienced by students and the ways that these students envision alternative practices that (...) support their digital engagement. Drawing upon student focus group interviews in a large American research and innovation intensive university, dialectical tensions identified include convenience–annoyance, integration–independence, and safety–insecurity, brought upon by students’ ongoing and prospective negotiations with Internet of Things. Furthermore, in a bid to understand students’ alternative data imaginaries, this project examined students’ preferred Internet of Things-related communication practices with campus digital application platforms, analog and older forms of digital media, as well as in-person interactions with traditional authorities within classroom and group settings. Finally, this contribution presents a discussion of the findings for theory and praxis, particularly for smart campus innovation and social data governance, in terms of potential growing challenges involving complexifying student privacy concerns, data normalization and coercion, and tertiary digital divides and inequalities. (shrink)

Personal data are produced through our daily interactions with digital technologies like search engines, social media, and online shopping, and is often referred to as our “digital exhaust.” It has been characterized as the key resource or asset for our economies in the 21st century. This paper focuses on the socio-technical imaginaries of digital personal data as a way to understand how desired forms of data governance are co-produced with collective understandings of personal data as (...) a political-economic asset. We examine the different socio-technical imaginaries that underpinned different developments in data regulations in the United States and EU from 2008 to 2016, focusing specifically on the mutual constitution of law, political economy, and technoscience. We do so in order to understand the “prehistories” of contemporary data governance. We analyze the institutional and legal context around the development of data privacy regulation and data commercialization in these two important jurisdictions and reflect on how this institutional and legal context configured their respective approaches to data governance. (shrink)

The current research examines the emergent literature of Critical Data Studies, and particularly aligns with Michael and Lupton’s (2016) manifesto calling for researchers to study the Public Understanding of Big Data. The aim of this paper is to explore Irish stakeholders’ narratives on data sharing in agriculture, and the ways in which their attitudes towards different data sharing governance models reflect their understandings of data, the impact that data hold in their lives and (...) in the farming sector, as well as their preferences for how data should be governed within agriculture. Seven focus groups were held in 2019 with Irish stakeholders from a variety of backgrounds, including agri-researchers, those working in SMEs, and farmers of varying ages and sectors. The primary activities carried out during these focus groups centred upon asking participants to discuss four different data sharing governance models, and to work their way through a set of value cards relating to these models. Focus group results are studied using an inductive, data-driven form of thematic analysis (Braun and Clarke 2006). Five primary themes cross-cut these focus groups: 1) Desire for a data intermediary, 2) Reversing the value chain, 3) Categorisation of data, 4) The common good, and 5) Potential danger in data sharing. These themes are explored in the paper through a detailed discussion of the focus group results, in which the authors track the manifestation of these themes across focus groups, and the ways they sometimes morphed or changed depending upon the participating stakeholder group. (shrink)

Background The ownership status of individual-level health data affects the manner in which it is used. In this paper we analyze two competing models of the ownership status of the data discussed in the literature recently: private ownership and public ownership. Main body In this paper we describe the limitations of these two models of data ownership with respect to individual-level health data, in particular in terms of ethical principles of justice and autonomy, risk mitigation, as (...) well as technological, economic, and conceptual issues. We argue that undifferentiated application of neither private ownership nor public ownership will allow us to resolve all the problems associated with effective, equitable, and ethical use of data. We suggest that, instead of focusing on data ownership, we should focus on the institutional and procedural aspects of data governance, such as using Data Access Committees (DACs) or equivalent managed access processes, which can balance the elements of these two ownership frameworks. Conclusion Undifferentiated application of the ownership concept (private or public) is not helpful in resolving problems associated with sharing individual-level health data. DACs or equivalent managed access processes should be an integral part of data governance. They can approve or disapprove data access requests after considering the potential benefits and harms to data subjects, their communities, primary researchers, and the wider society. (shrink)

It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in (...) recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader ‘health and wealth’ agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS. (shrink)

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary (...) analyzes Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program encompassing its proprietary TraceTogether app and physical token initially triggered vociferous public criticisms of Big Brother style surveillance. Using a dialogic communication framework, we analyze the TraceTogether initiative to interrogate the communicative strategies that were used to overcome public resistance. We argue that these strategies reflect a top-down approach that prioritizes transactional dissemination of information, in line with Singapore's technocratic stance toward governance. We further assert that such communicative tactics represent missed opportunities to foster public confidence in social data governance through greater trust building. We propose solutions for more dialogic communicative forms that build trust, so that officials can develop a sound understanding of the public concerns, increase the level of public engagement, and incorporate public feedback into policies that govern data use. (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)

The emergence of a global industry of digital health platforms operated by Big Tech corporations, and its growing entanglements with academic and pharmaceutical research networks, raise pressing questions on the capacity of current data governance models, regulatory and legal frameworks to safeguard the sustainability of the health research ecosystem. In this article, we direct our attention toward the challenges faced by the European General Data Protection Regulation in regulating the potentially disruptive engagement of Big Tech platforms in (...) health research. The General Data Protection Regulation upholds a rather flexible regime for scientific research through a number of derogations to otherwise stricter data protection requirements, while providing a very broad interpretation of the notion of “scientific research”. Precisely the breadth of these exemptions combined with the ample scope of this notion could provide unintended leeway to the health data processing activities of Big Tech platforms, which have not been immune from carrying out privacy-infringing and socially disruptive practices in the health domain. We thus discuss further finer-grained demarcations to be traced within the broadly construed notion of scientific research, geared to implementing use-based data governance frameworks that distinguish health research activities that should benefit from a facilitated data protection regime from those that should not. We conclude that a “re-purposing” of big data governance approaches in health research is needed if European nations are to promote research activities within a framework of high safeguards for both individual citizens and society. (shrink)

Government officials claim open data can improve internal and external communication and collaboration. These promises hinge on “data intermediaries”: extra-institutional actors that obtain, use, and translate data for the public. However, we know little about why these individuals might regard open data as a site of civic participation. In response, we draw on Ilana Gershon to conceptualize culturally situated and socially constructed perspectives on data, or “data ideologies.” This study employs mixed methodologies to examine (...) why members of the public hold particular data ideologies and how they vary. In late 2015 the authors engaged the public through a commission in a diverse city of approximately 500,000. Qualitative data was collected from three public focus groups with residents. Simultaneously, we obtained quantitative data from surveys. Participants’ data ideologies varied based on how they perceived data to be useful for collaboration, tasks, and translations. Bucking the “geek” stereotype, only a minority of those surveyed were professional software developers or engineers. Although only a nascent movement, we argue open data intermediaries have important roles to play in a new political landscape. (shrink)

BackgroundData processing of health research databases often requires a Data Protection Impact Assessment to evaluate the severity of the risk and the appropriateness of measures taken to comply with the European Union General Data Protection Regulation. We aimed to define and apply a comprehensive method for the evaluation of privacy, data governance and ethics among research networks involved in the EU Project Bridge Health.MethodsComputerised survey among associated partners of main EU Consortia, using a targeted instrument designed (...) by the principal investigator and progressively refined in collaboration with an international advisory panel. Descriptive measures using the percentage of adoption of privacy, data governance and ethical principles as main endpoints were used for the analysis and interpretation of the results.ResultsA total of 15 centres provided relevant information on the processing of sensitive data from 10 European countries. Major areas of concern were noted for: data linkage, access and accuracy of personal data and anonymisation procedures. A high variability was noted in the application of privacy principles.ConclusionsA comprehensive methodology of Privacy and Ethics Impact and Performance Assessment was successfully applied at international level. The method can help implementing the GDPR and expanding the scope of Data Protection Impact Assessment, so that the public benefit of the secondary use of health data could be well balanced with the respect of personal privacy. (shrink)

The importance of digital technologies for social and economic developments and a growing focus on data collection and privacy concerns have made the Internet a salient and visible issue in global politics. Recent developments have increased the awareness that the current approach of governments and business to the governance of the Internet and the adjacent technological spaces raises a host of ethical issues. The significance and challenges of the digital age have been further accentuated by a string of (...) highly exposed cases of surveillance and a growing concern about issues of privacy and the power of this new industry. This special issue explores what some have referred to as the “Internet-industrial complex”—the intersections between business, states, and other actors in the shaping, development, and governance of the Internet. (shrink)

New models of data governance for health data are a focus of growing interest in an era of challenge to the social licence. In this article, we reflect on what the data trust model, which is founded on principles of participatory governance, can learn from experiences of involving and engagement of members of the public and participants in the governance of large-scale biobanks. We distinguish between upstream and ongoing governance models, showing how they (...) require careful design and operation if they are to deliver on aspirations for deliberation and participation. Drawing on this learning, we identify a set of considerations important to future design for data trusts as they seek to ensure just, proportionate and fair governance. These considerations relate to the timing of involvement of participants, patterns of inclusion and exclusion, and responsiveness to stakeholder involvement and engagement. We emphasise that the evolution of governance models for data should be matched by a commitment to evaluation. (shrink)

The European Union Data Protection Regulation will have profound implications for public health, health services research and statistics in Europe. The EU Commission's Proposal was a breakthrough in balancing privacy rights and rights to health and healthcare. The European Parliament, however, has proposed extensive amendments. This paper reviews the amendments proposed by the European Parliament Committee on Civil Liberties, Justice and Home Affairs and their implications for health research and statistics. The amendments eliminate most innovations brought by the Proposal. (...) Notably, derogation to the general prohibition of processing sensitive data shall be allowed for public interests such as the management of healthcare services, but not health research, monitoring, surveillance and governance. The processing of personal health data for historical, statistical or scientific purposes shall be allowed only with the consent of the data subject or if the processing serves an exceptionally high public interest, cannot be performed otherwise and is legally authorised. Research, be it academic, government, corporate or market research, falls under the same rule. The proposed amendments will make difficult or render impossible research and statistics involving the linkage and analysis of the wealth of data from clinical, administrative, insurance and survey sources, which have contributed to improving health outcomes and health systems performance and governance; and may illegitimise efforts that have been made in some European countries to enable privacy-respectful data use for research and statistical purposes. If the amendments stand as written, the right to privacy is likely to override the right to health and healthcare in Europe. (shrink)

Scholars are becoming increasingly interested in whether and how government use of Big Data will affect public policy outcomes. Despite such growing scholarly interests, however, little evidence exists on the role Big Data can play in improving government service. We undertake one of the first quantitative studies revealing the potential utility and limitations of “Big Data-based policymaking” by exploring its recent use by the Seoul Metropolitan Government. In 2013, the government introduced the “Owl Bus”—a late-night bus system—the (...) routes of which were selected based on government analyses of Big Data relating to citizens’ late-night taxi and mobile phone use. The findings suggest that the average number of passengers utilizing the Owl Bus routes was significantly greater than that on other daytime bus routes with comparable characteristics. That said, we also present the potential limitations of evidence-based policymaking in general, especially when politics and equity considerations are factored in. (shrink)

The paper deals with the governance of Unmanned Aircraft Systems in European law. Three different kinds of balance have been struck between multiple regulatory systems, in accordance with the sector of the governance of UAS which is taken into account. The first model regards the field of civil aviation law and its European Union ’s regulation: the model looks like a traditional mix of top-down regulation and soft law. The second model concerns the EU general data protection (...) law, the GDPR, which has set up a co-regulatory framework summed up with the principle of accountability also, but not only, in the field of drones. The third model of governance has been adopted by the EU through methods of legal experimentation and coordination mechanisms for UAS. The overall aim of the paper is to elucidate the ways in which such three models interact, insisting on differences and similarities with other technologies, and further legal systems. (shrink)

Big Data’s calculative ontology relies on and reproduces a form of hyperindividualism in which the ontological unit of analysis is the discrete data point, the meaning and identity of which inheres in itself, preceding, separate, and independent from its context or relation to any other data point. The practice of Big Data governed by an ontology of hyperindividualism is also constitutive of that ontology, naturalizing and diffusing it through practices of governance and, from there, throughout (...) myriad dimensions of everyday life. In this paper, I explicate Big Data’s ontology of hyperindividualism by contrasting it to a coconstitutive ontology that prioritizes relationality, context, and interdependence. I then situate the ontology of hyperindividualism in its genealogical context, drawing from Patrick Joyce’s history of liberalism and John Dewey’s pragmatist account of individualism, liberalism, and social action. True to its genealogical provenance, Big Data’s ontological politics of hyperindividualism reduces governance to the management of atomistic behavior, undermines the contribution of urban complexity as a resource for governance, erodes the potential for urban democracy, and eviscerates the possibility of collective resistance. (shrink)

Empirical evidence suggests that while people hold the capacity to control their data in high regard, they increasingly experience a loss of control over their data in the online world. The capacity to exert control over the generation and flow of personal information is a fundamental premise to important values such as autonomy, privacy, and trust. In healthcare and clinical research this capacity is generally achieved indirectly, by agreeing to specific conditions of informational exposure. Such conditions can be (...) openly stated in informed consent documents or be implicit in the norms of confidentiality that govern the relationships of patients and healthcare professionals. However, with medicine becoming a data-intense enterprise, informed consent and medical confidentiality, as mechanisms of control, are put under pressure. In this paper we explore emerging models of informational control in data-intense healthcare and clinical research, which can compensate for the limitations of currently available instruments. More specifically, we discuss three approaches that hold promise in increasing individual control: the emergence of data portability rights as means to control data access, new mechanisms of informed consent as tools to control data use, and finally, new participatory governance schemes that allow individuals to control their data through direct involvement in data governance. We conclude by suggesting that, despite the impression that biomedical big data diminish individual control, the synergistic effect of new data management models can in fact improve it. (shrink)

Spurred by a confluence of factors, most notably the decreasing cost of high-throughput technologies and advances in information technologies, a number of population research initiatives have emerged in recent years. These include large-scale, internationally collaborative genomic projects and biobanks, the latter of which can be defined as an organized collection of human biological material and associated data stored for one or more research purposes. Biobanks are a key emerging research infrastructure, and those established as prospective research resources comprising biospecimens (...) and data from many participants are viewed as particularly promising drivers of biomedical progress. Such biobanks, particularly those publicly funded and set up to promote the public interest, have expanded across the globe in recent years. (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)

Data is essential to governing those emerging matters of concern that confront the agrifood every day. But data is no neutral intermediary. It disrupts, exposes, and creates new social, economic, political, and environmental possibilities, whilst simultaneously hiding, excluding, and foreclosing others. Scholars have become attuned to both the constitutive role of data in creating everyday worlds, and the need to develop critical accounts of the materialities, spatialities and multiplicities of data relationships. Whereas this emerging work develops (...) insight to the capacity for data topologies to reterritorialise the spatial performances of everyday life, it has largely reduced the associated temporal dimensions to matters of fact. The effect of these performances has been to naturalize the temporal quality of speed and elide the multiple temporalities required to enact contemporary data worlds. Applying the lenses of infrastructuring, performativity and ferality, this paper explores temporality and data in the everyday worlds produced through the New Zealand kiwifruit industry’s focus on dry matter. The paper argues that temporalities are deeply embedded in the kiwifruit industry’s data relations. We show that while temporal data relations are critical to the industry, we also highlight ways in which those relations introduce new, potentially destabilizing performances into kiwifruit relations. (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...

Denmark is a society that has already moved towards Big Data and a Learning Health Care System. Data from routine healthcare has been registered centrally for years, there is a nationwide tissue bank, and there are numerous other available registries about education, employment, housing, pollution, etcetera. This has allowed Danish researchers to study the link between exposures, genetics and diseases in a large population. This use of public registries for scientific research has been relatively uncontroversial and has been (...) supported by facilitative regulation that allows data to be used without the consent of data subjects. However, in the future much of the data will not be held by public authorities but by private companies. What are the implications of this shift for the governance of the research use of the data? This paper will argue that increased involvement of Research Ethics Committees and better training of researchers are necessary; and that some form of consent will have to be re-introduced. Four different consent models will be discussed: Opt-Out, Broad/Blanket consent, Dynamic consent, and Meta consent. It will be argued that a governance model including a possibility for citizens to make meta-choices strikes the best balance between individual and public interests. (shrink)

Although digital technologies often receive the bulk of media and academic attention, there is another crucial aspect of the data revolution in agriculture: governance frameworks for collecting and analyzing data. Metrics are increasingly being used to facilitate the collection of data and convert it into useful forms. While there is growing interest in using metrics and data to enhance the sustainability of food and agriculture, there is a lack of research on how metrics are put (...) into practice and to what degree their use leads to improved sustainability. To address this knowledge gap, this article examines the implementation of sustainability metrics in produce agriculture in the United States. Specifically, the focus is on the processes of on-farm metrics use– data generation, data analysis, and data-based decision-making. We find that implementing metrics involves several tensions for farmers, including metrics’ ability to capture the complexity of produce agriculture, how metrics align with farmers’ tacit and experiential knowledge, and the impact of metrics on farmer autonomy. Our findings suggest that for the use of metrics to improve sustainability it is crucial to understand how social, cultural, and material conditions intersect with metrics. To better account for such conditions, farmers need more voice in how metrics are implemented and data is used. (shrink)

Global health emergencies often lead to a proliferation of health-related research and resultant data, which is shared across borders to help control the outbreak of disease and support decision-making regarding public health interventions. However, efforts to share data can be hindered by diverse international ethical and legal frameworks. The frameworks aim to govern coordinated processing, sharing and transfer of health data across borders thus placing burdens on researchers who are willing or obligated to share data. In (...) this paper, we investigate ethical values and legal principles that can be applied to ensure a more streamlined approach in the governance of cross-border health data sharing during global health emergencies. A scoping review approach was used to better understand emerging evidence about issues related to the ethical and legal governance of cross-border health data sharing. We first highlight the challenges of sharing health data across borders. We then discuss how the challenges might be overcome through the application of ethical values and legal principles that have been identified in literature. This is followed by a proposal for possible ways of streamlining the governance of health data sharing from ethical and legal perspectives in a bid to better prepare for future global health emergencies and promote the responsible sharing of health data. (shrink)

As the capacity to generate, store, aggregate and combine ever greater volumes and types of data about individuals, behaviours and interactions continues to expand apace, so too does the challenge of ensuring suitable and appropriate governance of that data. In broad terms, the challenge is simple; how to ensure the benefits of data, such as improvements in service delivery or individual and collective well-being, while avoiding harms such as discrimination, injustice or placing undue burdens on individuals (...) and groups. The difficulty, as ever, lies in the details. As Nicol et al 1 observe, informed consent is important but does not replace the need for an appropriate governance framework that covers the wider ecosystem of data generation, use and reuse. Moreover, there is no one universal oversight mechanism for data sharing, but what counts as ‘appropriate’ must take into account the context and purpose of data use. In this regard, the study by Ford et al is an important and valuable contribution to our understanding of the …. (shrink)

Background: Recent changes to regulatory guidance in the US and Europe have complicated oversight of secondary research by rendering most uses of de-identified data exempt from human subjects oversight. To identify the implications of such guidelines for harms to participants and communities, this paper explores the secondary uses of one de-identified DNA sample collection with limited oversight: the Human Genome Diversity Project (HGDP)-Centre d'Etude du Polymorphisme Humain, Fondation Jean Dausset (CEPH) Human Genome Diversity Panel. Methods: Using a combination of (...) keyword and cited reference search, we identified English-language scientific articles published between 2002 and 2009 that reported analysis of HGDP Diversity Panel samples and/or data. We then reviewed each article to identify the specific research use to which the samples and/or data was applied. Secondary uses were categorized according to the type and kind of research supported by the collection. Results: A wide variety of secondary uses were identified from 148 peer-reviewed articles. While the vast majority of these uses were consistent with the original intent of the collection, a minority of published reports described research whose primary findings could be regarded as controversial, objectionable, or potentially stigmatizing in their interpretation. Conclusions: We conclude that potential risks to participants and communities cannot be wholly eliminated by anonymization of individual data and suggest that explicit review of proposed secondary uses, by a Data Access Committee or similar internal oversight body with suitable stakeholder representation, should be a required component of the trustworthy governance of any repository of data or specimens. (shrink)

While the academic separation of policing and regulation is still largely operative, points of convergence are more significant than ever in the digital age, starting with concomitant debates about algorithms as a new figure of power. From the policing of illegal activities to the regulation of legal ones, the algorithmization of such critical social ordering practices has been the subject of growing attention. These burgeoning discussions are focused on one common element: big data surveillance. In accordance with such similarities (...) and paralleled developments in policing and regulation, the article aims to further bridge the gap between literatures to respond to the calls for studying big data surveillance across institutional domains and social fields. To do so, it is focused on one case study that articulates algorithmic policing and regulation domains, in-between security and economic fields. This is the fight against illicit finance, i.e. ‘the global action against the financial flows that fuel crime and terrorism’. To what extent does big data surveillance make a difference in the main global policy of crime-fighting and financial regulation? Drawing on a fieldwork in a large North American bank, the present article takes stock of the algorithmic overlap between policing and regulation. It argues that the final result is policing and regulation of neither too much nor too little, which gives rise to automated and everyday mass surveillance while remaining as far removed from regulatory and crime-fighting ambitions as it is from dystopian visions of big data and algorithmic drama. (shrink)

Government transparency and accountability during Covid 19: The data underpinning decisions.

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)

Biodiversity scientists often describe their field as aiming to save life and humanity, but the field has yet to reckon with the history and contemporary practices of colonialism and systematic racism inherited from natural history. The online data portals scientists use to store and share biodiversity data are a growing class of organizations whose governance can address or perpetuate and further institutionalize the implicit assumptions and inequitable social impacts from this extensive history. In this context, researchers and (...) Indigenous Peoples are developing and implementing new strategies to examine and change assumptions about which agents should count as salient participants to scientific projects, especially projects that build and manage large digital data portals. Two notable efforts are the FAIR and CARE Principles for scientific data management and governance. To characterize how these influence the governance of biodiversity data portals, we develop an account of fitness-for-use that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity data projects, 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 salient participants to data science. (shrink)

With the continuous development of the era of big data, community management plays an important role in people’s mental health. Improve people’s mental health through the use of big data to improve governance of the community. To solve the problem, sequence segmentation is used in feature extraction, histogram absolute difference calculation and K-means intelligent algorithm, to analyze the existing problems one by one. Research shows that it is possible to systematically govern the community through big data (...) and intelligent computing, so that community residents can live a higher quality of life. In order to get rid of psychological problems, it can be solved by establishing special psychological support institutions in the community and strengthening the role of the government in community management. With the continuous development of big data, the application of big data and intelligence in community governance and mental health community is a trend. With the continuous progress of society, it also brings pressure to people, which leads to the decline of people’s mental health quality. We need to do something to improve community governance and people’s mental health. (shrink)

본 논문의 목적은 보건의료 빅데이터의 윤리적 활용에 적합한 거버넌스 모델을 제시하는 것이다. 다시 말해서 ‘보건의료 빅데이터에 다른 가치를 부여하고 있는 다양한 이해당사자들, 예를 들어 개별 보건의료 데이터의 주체, 의생명과학자, 보건의료 빅데이터로 영리를 추구하는 기업들, 그리고 일반 대중 등이 공유할 수 있는 공적 가치를 보호하고 균형을 잡을 수 있는 거버넌스는 어떤 모델이어야 하는가?’를 해명하고자 한다. 이를 위해 먼저 생명의료윤리 영역에서 보건의료 빅데이터의 공적 가치의 의미를 분석하고, 어떤 윤리적 거버넌스 모델이 공적 가치를 보호하는 데에 적합한지 검토한다. 본 논문은 정직한 중개인 기관(honest broker) (...) 모델에 수평적 네트워크의 형성을 추가하는 방식의 거버넌스 모델을 제시하고자 한다. (shrink)

Developments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General (...) class='Hi'>Data Protection Regulation (GDPR) within a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research—among researchers and with data subjects—is grounded in trust, which should be more explicitly recognised in the governance of health data research. (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)

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 increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. (...) The framework consists of four primary areas of regulatory intervention: binding regulation, ethics and soft law, responsible innovation, and human rights. (shrink)