Bulk collection involves the mining of large data sets containing personal data, often for a security purpose. In 2013, Edward Snowden exposed large scale bulk collection on the part of the US National Security Agency as part of a secret counter-terrorism effort. This effort has mainly been criticised for its invasion of privacy. I argue that the right moral argument against it is not so much to do with intrusion, as ineffectiveness for its official purpose and (...) the lack of oversight by security cleared legislators. It also lends itself to government "domination" (in Pettit's sense) of those who are subject to it. (shrink)

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, and more. At best, data about your health might (...) end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. -/- This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.). (shrink)

Rapid advancements in machine learning techniques allow mass surveillance to be applied on larger scales and utilize more and more personal data. These developments demand reconsideration of the privacy-security dilemma, which describes the tradeoffs between national security interests and individual privacy concerns. By investigating mass surveillance techniques that use bulk data collection and machine learning algorithms, we show why these methods are unlikely to pinpoint terrorists in order to prevent attacks. The diverse characteristics of terrorist attacks—especially when considering (...) lone-wolf terrorism—lead to irregular and isolated footprints. The irregularity of data affects the accuracy of machine learning algorithms and the mass surveillance that depends on them which can be explained by three kinds of known problems encountered in machine learning theory: class imbalance, the curse of dimensionality, and spurious correlations. Proponents of mass surveillance often invoke the distinction between collecting data and metadata, in which the latter is understood as a lesser breach of privacy. Their arguments commonly overlook the ambiguity in the definitions of data and metadata and ignore the ability of machine learning techniques to infer the former from the latter. Given the sparsity of datasets used for machine learning in counterterrorism and the privacy risks attendant with bulk data collection, policymakers and other relevant stakeholders should critically re-evaluate the likelihood of success of the algorithms and the collection of data on which they depend. (shrink)

Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed (...) data raises unique questions regarding the interplay of multiple regulatory systems alongside food safety legislation. Provisions aiming to preserve the confidentiality of data and protect personal information are juxtaposed to norms prescribing the public disclosure of scientific information. This research is intended to provide guidance for data governance and data ownership issues that unfold from the ongoing transformation of the technical and legal domains of food safety risk assessment. Following the reconstruction of technological advances in data collection and analysis and the description of recent amendments to food safety legislation, emerging concerns are discussed in light of the individual, collective and social implications of the deployment of cutting-edge Big Data collection and analysis techniques. Then, a set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment. The proposed set of recommendations adopts Safety, Accountability, Fairness, Explainability, Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle. (shrink)

Ethical challenges in genomic epidemiology are the direct result of novel tools used to confront scientific challenges in the field. An orders-of-magnitude increase in scale of genetic data collection has created the need for establishing diffuse international partnerships, sometimes across developed- and developing-world countries, with ramifications for assigning research ownership, distributing intellectual property rights, and encouraging capacity-building. Meanwhile, the fact that genomic epidemiological research is so far upstream in the pipeline of therapy development has implications for the privacy rights (...) of research participants and for a rigorous definition of valid informed consent, particularly in resource-poor settings. From these scientific underpinnings, we distill out two main categories of ethical issues: (1) How should researchers ensure that the subjects of research are appropriately protected? and (2) What is the structure of an equitable and fair system for distributing the financial and scientific rewards of research? We attempt to delineate the contours of specific problems in each category and propose steps toward solutions with reference to a particular project, known as gMap.net, that focuses on genomic epidemiological studies of malaria. (shrink)

In earlier work, I have expressed scepticism about privacy-based criticisms of bulk collection for counter-terrorism ( Sorell 2018 ). But even if these criticisms are accepted, is bulk collection nonetheless legitimate on balance – because of its operational utility for the security services, and the overriding importance of the purposes that the security services serve? David Anderson’s report of the Bulk Powers review in the United Kingdom suggests as much, provided bulk collection complies (...) with strong legal safeguards ( Anderson 2016 ). I think it is hard to mount a uniformly compelling operational utility argument, because purposes other than counter-terrorism are pursued by the security services with the help of bulk collection. For example, the Intelligence Services Act 1994, section 1(2) says that apart from the interests of national security and the prevention and detection of serious crime, the Secret Intelligence Service may act “in the interests of the economic well-being of the United Kingdom.”The phrase “interests of the economic well-being of the United Kingdom” is opento a disturbingly wide range of interpretations, and it is doubtful that these interests justify bulk collection even when relevant “economic interests” are confined to those “also relevant to the interests of national security”, as required by the Investigatory Powers Act (2016) section 204 (3a). (shrink)

The focus of this paper are the ethical, legal and social challenges for ensuring the responsible use of “big brain data”—the recording, collection and analysis of individuals’ brain data on a large scale with clinical and consumer-directed neurotechnological devices. First, I highlight the benefits of big data and machine learning analytics in neuroscience for basic and translational research. Then, I describe some of the technological, social and psychological barriers for securing brain data from unwarranted access. In this context, I (...) then examine ways in which safeguards at the hardware and software level, as well as increasing “data literacy” in society, may enhance the security of neurotechnological devices and protect the privacy of personal brain data. Regarding ethical and legal ramifications of big brain data, I first discuss effects on the autonomy, the sense of agency and authenticity, as well as the self that may result from the interaction between users and intelligent, particularly closed-loop, neurotechnological devices. I then discuss the impact of the “datafication” in basic and clinical neuroscience research on the just distribution of resources and access to these transformative technologies. In the legal realm, I examine possible legal consequences that arises from the increasing abilities to decode brain states and their corresponding subjective phenomenological experiences on the hitherto inaccessible privacy of these information. Finally, I discuss the implications of big brain data for national and international regulatory policies and models of good data governance. (shrink)

Surveillance capitalism companies, such as Google and Facebook, have substantially increased the amount of information collected, analyzed, and monetized, including health information increasingly used in precision medicine research, thereby presenting great challenges for health privacy.

This essay details the resurgence of wellness program as employed by large corporations with the aim of reducing healthcare costs. The essay narrows in on a discussion of how Big Data collection practices are being utilized in wellness programs and the potential negative impact on the worker in regards to privacy and employment discrimination. The essay offers an ethical framework to be adopted by wellness program vendors in order to conduct wellness programs that would achieve cost-saving goals without undue (...) burdens on the worker. The essay also offers some innovative approaches to wellness that may well better serve the goals of healthcare cost reduction. (shrink)

This article introduces an important concept: Transparency by way of Humanistic Intelligence as a human right, and in particular, Big/little Data and Sur/sous Veillance, where “Little Data” is to sousveillance as “Big Data” is to surveillance. Veillance is a core concept not just in human–human interaction but also in terms of Human–Computer Interaction. In this sense, veillance is the core of Human-in-the-loop Intelligence, leading us to the concept of “Sousveillant Systems” which are forms of Human–Computer Interaction in which internal computational (...) states are made visible to end users, allowing users to “jump” into the computational feedback loop whenever or wherever they want. An important special case of Sousveillant Systems is that of scientific exploration: not only is data considered, but also due consideration must be given to how data is captured, understood, explored, and discovered, and in particular, to the use of scientific instruments to collect data and to make important new discoveries, and learn about the world. Science is a domain where bottom-up transparency is of the utmost importance, and scientists have the right and responsibility to be able to understand the instruments that they use to make their discoveries. Such instruments must be sousveillant systems! (shrink)

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

Big data, artificial intelligence, and increasingly precise biometric techniques have given state and private organizations unprecedented scope and power for the surveillance and dataveillance of migrants. In many cases, these technologies have evolved faster than our legal, political, and ethical mechanisms. This paper, drawing on current discussions of justice and non-domination, proposes a non-domination-based ethics of digital surveillance and mobility, in which the legitimacy of these technologies depends on their avoidance of the arbitrary use of power. This allows us to (...) ethically assess new technologies and justify juridical, democratic, and administrative mechanisms. (shrink)

Purpose – Ubiquitous computing and “big data” have been widely recognized as requiring new concepts of privacy and new mechanisms to protect it. While improved concepts of privacy have been suggested, the paper aims to argue that people acting in full conformity to those privacy norms still can infringe the privacy of others in the context of ubiquitous computing and “big data”. Design/methodology/approach – New threats to privacy are described. Helen Nissenbaum's concept of “privacy as contextual integrity” is reviewed concerning (...) its capability to grasp these problems. The argument is based on the assumption that the technologies work, persons are fully informed and capable of deciding according to advanced privacy considerations. Findings – Big data and ubiquitous computing enable privacy threats for persons whose data are only indirectly involved and even for persons about whom no data have been collected and processed. Those new problems are intrinsic to the functionality of these new technologies and need to be addressed on a social and political level. Furthermore, a concept of data minimization in terms of the quality of the data is proposed. Originality/value – The use of personal data as a threat to the privacy of others is established. This new perspective is used to reassess and recontextualize Helen Nissenbaum's concept of privacy. Data minimization in terms of quality of data is proposed as a new concept. (shrink)

Farming is undergoing a digital revolution. Our existing review of current Big Data applications in the agri-food sector has revealed several collection and analytics tools that may have implications for relationships of power between players in the food system. For example, Who retains ownership of the data generated by applications like Monsanto Corproation's Weed I.D. “app”? Are there privacy implications with the data gathered by John Deere's precision agricultural equipment? Systematically tracing the digital revolution in agriculture, and charting the (...) affordances as well as the limitations of Big Data applied to food and agriculture, should be a broad research goal for Big Data scholarship. Such a goal brings data scholarship into conversation with food studies and it allows for a focus on the material consequences of big data in society. (shrink)

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

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

Tennis players have more physical training content, and the training items are complex. For athletes, training programs that adapt to their individual characteristics should be formulated according to their physical characteristics. The current development of big data has brought about changes in thinking, management, and business models. The combination of complex systems and big data can also make breakthroughs in the sports field. Based on this, this article proposes a tennis player training schedule intelligent formulation system based on complex system (...) big data. First of all, this article adopts the literature data method, comparative analysis method, experimental analysis method, etc., in-depth study of the concepts of big data, complex system, and the physical structure characteristics of tennis sports. This paper designs an intelligent system for making tennis players’ training schedule, which collects, transforms, and integrates tennis training data through the characteristics of big data. Then, the dynamic time regulation of tennis is performed through a complex system, and finally, the experimental system is analyzed. This article mainly analyzes the comparison of physical indicators between the experimental group and the control group before and after the experiment, the evaluation indicators of sports events, the strength training effects of tennis events, and the analysis of shoulder joint tests. There is no significant difference between the experimental group and the control group in the items before the experiment, P > 0.05, which suggests that the physical fitness of the two groups of athletes is similar; in the posttest data, the experimental group and the control group have significant differences, P < 0.05, indicating an effect from the experiment. In particular, in fan running, forward and backward strokes, and the serve, the scores of the experimental group were higher than those of the control group, indicating that the use of the formulated training system demonstrated significant results. (shrink)

The aim of this work is to identify and analyze a set of challenges that are likely to be encountered when one embarks on fieldwork in linguistic communities that feature small, young, and/or non-standard languages with a goal to elicit big sets of rich data. For each challenge, we (i) explain its nature and implications, (ii) offer one or more examples of how it is manifested in actual linguistic communities, and (iii) where possible, offer recommendations for addressing it effectively. Our (...) list of challenges involves static characteristics (e.g., absence of orthographic conventions and how it affects data collection), dynamic processes (e.g., speed of language change in small languages and how it affects longitudinal collection of big amounts of data), and interactive relations between non-dynamic features that are nevertheless subject to potentially rapid change (e.g., absence of standardized assessment tools or estimates for psycholinguistic variables). The identified challenges represent the domains of data collection and handling, participant recruitment, and experimental design. Among other issues, we discuss population limits and degree of power, inter- and intraspeaker variation, absence of metalanguage and its implications for the process of eliciting acceptability judgments, and challenges that arise from absence of local funding, conflicting regulations in relation to privacy issues, and exporting large samples of data across countries. Finally, the ten experimental challenges presented cover a broad typological spectrum, encompassing both spoken and sign, extant and nearly extinct languages. (shrink)

Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. ‘Big data’ has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (...) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a ‘data society’. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated. (shrink)

This paper offers a sketch of the complicated conflicts which arise—and metastasize seemingly daily—in the era of Big Data. Given the public’s ubiquitous-yet-ostensibly-voluntary data surrender, and industry’s ubiquitous-yet-ostensibly-anodyne collection of the same, inaction is not an option for any near-just society. By revisiting the philosophical basis for Panoptic apparatus, sketching the tumultuous history of US contract law trying to protect the public from itself, and comparing existing industry codes for similarly-situated—read: terrifyingly invasive—fields, the paper will provide a preliminary framework (...) for identifying and confronting the galaxy of problems associated with data analytics. (shrink)

Big data techniques, data-driven science and their technological applications raise many serious ethical questions, notably about privacy protection. In this paper, we highlight an entanglement between epistemology and ethics of big data. Discussing the mobilisation of big data in the fields of biomedical research and health care, we show how an overestimation of big data epistemic power – of their objectivity or rationality understood through the lens of neutrality – can become ethically threatening. Highlighting the irreducible non-neutrality at play in (...) big data tools, we insist upon the ethical importance of a critical epistemological approach in which big data are understood as possibly valuable only when coupled with human intelligence and evaluative rationality. (shrink)

In The Black Box Society, Frank Pasquale develops a critique of asymmetrical power: corporations’ secrecy is highly valued by legal orders, but persons’ privacy is continually invaded by these corporations. This response proceeds in three stages. I first highlight important contributions of The Black Box Society to our understanding of political and legal relationships between persons and corporations. I then critique a key metaphor in the book, and the role of transparency and ‘watchdogging’ in its primary policy prescriptions. I then (...) propose ‘relational selfhood’ as an important new way of theorizing interdependence in an era of artificial intelligence and Big Data, and promoting optimal policies in these spheres. (shrink)

The second-wave feminist critique of privacy defies the liberal opposition between the public-political and the private-personal. Feminist thinkers such as Hanisch, Young or Fraser note that, according to this liberal conception, public institutions often keep asymmetric power relations between private agents away from political discussion and action. The resulting subordination of some agents to others tends, therefore, to be naturalised and redefined as a «personal problem». Drawing on these contributions, this article reviews the social and political implications of big data (...) exploitation and questions whether personal data protection must remain a matter of «privacy self-management». It aims to show that feminist political theory can decidedly help to identify and tackle the root causes of what I call «data domination».La crítica feminista de segunda ola a la privacidad desafía la oposición liberal entre lo público-político y lo privado-personal. Pensadoras feministas como Hanisch, Young o Fraser señalan que, de acuerdo con esta concepción liberal, las instituciones públicas a menudo dejan fuera de la discusión y la acción política las relaciones de poder asimétricas entre agentes privados. La resultante subordinación de unos agentes a otros tiende, por tanto, a ser naturalizada y redefinida como un «problema personal». Basándose en estas contribuciones, este artículo revisa las implicaciones sociales y políticas de la explotación de datos masivos y cuestiona si la protección de los datos personales debe continuar siendo una cuestión de «auto-gestión de la privacidad». Su objetivo es mostrar que la teoría política feminista puede ayudar decididamente a identificar y enfrentar las causas de lo que llamo «dominación a través de los datos». (shrink)

Today’s economy has transitioned from the traditional brick and mortar structure of doing business to that of digitalized economy. The latter functions with the aid of technological tools with ‘data’ being the most significant tool in today’s context. The issue has become even more critical with the advent of ‘big data’. It is argued that accumulation, analysis and usage of ‘big data’ enable creation of varied forms of entry barriers for new entrants and information asymmetries for customers which in turn (...) affect ‘market competition’ adversely (Santesteban & Longpre The Antitrust Bulletin,65(3), 459-485, 2020; Fast et al., 2021). Consequently, it is pertinent to revisit the traditional understanding of ‘market competition’ as stipulated in the antitrust laws across jurisdictions and specifically the Indian Competition Act, 2002. The existing regime being centred around neo-classical price theory is not equipped to comprehend the various dimensions of the digitalised world where ‘data’ is the new form of currency having effects on market power and, consequently, on market competition. Digital markets are multi-sided and non-linear in nature where ‘big data’ acts as a lubricant for their smooth functioning. Whether access to data provides any form of competitive advantage is the central question. A resource to provide any form of competitive advantage must be distinct, rare, inimitable, non-substitutable and valuable (Barney, 1991). If data carries such characteristics, the continuous percolation and usage of technology powered by ‘big data’ into the markets will affect the understanding of the basic competitive wrongs. Potential interdependencies between technology, data and market power also affect the understanding of ‘dominance’ as stipulated under the existing law. The unique challenges which ‘big data’ poses are not only restricted to competition policy but also extended to enforcement as they trigger overlapping constitutional concerns in relation to privacy. This demands for looking at the existing gap between competition law/policy and the ‘data’-driven challenges being faced in this ever-evolving digital economy. (shrink)

The advent of artificial intelligence (AI) challenges political theorists to think about data ownership and policymakers to regulate the collection and use of public data. AI producers benefit from free public data for training their systems while retaining the profits. We argue against the view that the use of public data must be free. The proponents of unconstrained use point out that consuming data does not diminish its quality and that information is in ample supply. Therefore, they suggest, publicly (...) available data should be free. We present two objections. First, allowing free data use promotes unwanted inequality. Second, contributors of information did not and could not anticipate that their contribution would be used to train AI systems. Therefore, charging for extensive data use is pro tanto permissible and desirable. We discuss policy implications and propose a progressive data use tax to counter the inequality arising. (shrink)

The rise of Big Data changes the context in which organisations producing official statistics operate. Big Data provides opportunities, but in order to make optimal use of Big Data, a number of challenges have to be addressed. This stimulates increased collaboration between National Statistical Institutes, Big Data holders, businesses and universities. In time, this may lead to a shift in the role of statistical institutes in the provision of high-quality and impartial statistical information to society. In this paper, the changes (...) in context, the opportunities, the challenges and the way to collaborate are addressed. The collaboration between the various stakeholders will involve each partner building on and contributing different strengths. For national statistical offices, traditional strengths include, on the one hand, the ability to collect data and combine data sources with statistical products and, on the other hand, their focus on quality, transparency and sound methodology. In the Big Data era of competing and multiplying data sources, they continue to have a unique knowledge of official statistical production methods. And their impartiality and respect for privacy as enshrined in law uniquely position them as a trusted third party. Based on this, they may advise on the quality and validity of information of various sources. By thus positioning themselves, they will be able to play their role as key information providers in a changing society. (shrink)

Intelligence on mass media audiences was founded on representative statistical samples, analysed by statisticians at the market departments of media corporations. The techniques for aggregating user data in the age of pervasive and ubiquitous personal media build on large aggregates of information analysed by algorithms that transform data into commodities. While the former technologies were built on socio-economic variables such as age, gender, ethnicity, education, media preferences, Big Data technologies register consumer choice, geographical position, web movement, and behavioural information in (...) technologically complex ways that for most lay people are too abstract to appreciate the full consequences of. The data mined for pattern recognition privileges relational rather than demographic qualities. We argue that the agency of interpretation at the bottom of market decisions within media companies nevertheless introduces a ‘heuristics of the algorithm’, where the data inevitably becomes translated into social categories. In the paper we argue that although the promise of algorithmically generated data is often implemented in automated systems where human agency gets increasingly distanced from the data collected, one can observe a felt need among media users and among industry actors to ‘translate back’ the algorithmically produced relational statistics into ‘traditional’ social parameters. The tenacious social structures within the advertising industries work against the techno-economically driven tendencies within the Big Data economy. (shrink)

This edited collection brings together a series of interdisciplinary contributions in the field of Information Technology Law. The topics addressed in this book cover a wide range of theoretical and practical legal issues that have been created by cutting-edge Internet technologies, primarily Big Data, the Internet of Things, and Cloud computing. Consideration is also given to more recent technological breakthroughs that are now used to assist, and - at times - substitute for, human work, such as automation, robots, sensors, (...) and algorithms. The chapters presented in this edition address these issues from the perspective of different legal backgrounds. The first part of the book discusses some of the shortcomings that have prompted legislators to carry out reforms with regard to privacy, data protection, and data security. Notably, some of the complexities and salient points with regard to the new European General Data Protection Regulation (EU GDPR) and the new amendments to the Japan's Personal Information Protection Act (PIPA) have been scrutinized. The second part looks at the vital role of Internet intermediaries (or brokers) for the proper functioning of the globalized electronic market and innovation technologies in general. The third part examines an electronic approach to evidence with an evaluation of how these technologies affect civil and criminal investigations. The authors also explore issues that have emerged in e-commerce, such as Bitcoin and its blockchain network effects. The book aims to explain, systemize and solve some of the lingering legal questions created by the disruptive technological change that characterizes the early twenty-first century. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and approval. There (...) are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and (...) re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. (shrink)

Aims To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners. Methods We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants’ responses were showcased using descriptive statistics and compared between groups using t-test or ANOVA. (...) Results We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of “broad consent”, respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI’s abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare. Conclusion Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology. (shrink)

This article aims to present the problem of the impact of artificial intelligence on respect for human dignity in the sphere of care for people who, for various reasons, are described as particularly vulnerable, especially seniors and people with various disabilities. In recent years, various initiatives and works have been undertaken on the European scene to define the directions in which the development and use of artificial intelligence should go. According to the human-centric approach, artificial intelligence should be developed, used (...) and monitored with people in mind, their needs and rights. It is artificial intelligence that should adapt to the rules set by people, not the other way around. The source of all human rights and freedoms is respect for human dignity. This is evidenced by numerous European, international, national regulations and documents. Respecting this value is also one of the works on the AI development. One of the areas of our life into which AI enters more and more boldly and which in the near future, due to demographic reasons and aging population, may be almost dominated by modern technologies, is the medical and care system. The usefulness of industrial co-robots made us think about the possibility of using similar devices in the non-industrial sphere, including in the sphere of care, nursing and therapy. Actually, despite the impressive development of technology and AI, intelligent robots/devices used for care, nursing or therapy (so-called care/nursing or therapy robots) perform rather simple activities. They provide information, navigate, bring something and give it (e.g. medicines, food), help to get up. They are an important support for medical staff in this respect. Performing tasks that are more complex in terms of technology and movement, such as feeding, washing, intimate hygiene, dressing or undressing, is still beyond the reach of the currently used robots. The question we must ask ourselves is whether, in the future, care robots will be able to perform such tasks, and should they do so? Will this lead to the replacement of human medical personnel in the performance of the above-mentioned activities with such robots. Would anyone want to be "looked after" by a soulless machine? Should potential wards/patients, in other words all of us in fact, have the right to object to being taken into the care of an intelligent robot? There is no doubt that intelligent robots have great potential, especially when it comes to ensuring or maintaining the independence or mobility of people with various health limitations. However, we must not forget about the risks and dangers they may generate. Even a simple robot generates serious legal and ethical problems. Who should be liable for damage caused in connection with improper care performed with the participation of a robot? Robots based on AI systems are able to collect a significant amount of sensitive data, for example through the face and speech recognition function. There is considerable scope for abuse in this area, not only in terms of personal data protection but also in terms of informational self-determination, privacy and dignity. Bearing in mind the above risks, the creation of a new right to object to being looked after by a robot should be considered. (shrink)

There has been a spurt in both fundamental and translational research that examines the underlying mechanisms of the human microbiome in psychiatric disorders. The personalized and dynamic features of the human microbiome suggest the potential of its manipulation for precision psychiatry in ways to improve mental health and avoid disease. However, findings in the field of microbiome also raise philosophical and ethical questions. From a philosophical point of view, they may yet be another attempt at providing a biological cause for (...) phenomena that ultimately cannot be so easily localized. From an ethical point of view, it is relevant that the human gut microbiome comprises data on the individual’s lifestyle, disease history, previous medications, and mental health. Massive datasets of microbiome sequences are collected to facilitate comparative studies to identify specific links between the microbiome and mental health. Although this emerging research domain may show promise for psychiatric patients, it is surrounded by ethical challenges regarding patient privacy, health risks, effects on personal identity, and concerns about responsibility. This narrative overview displays the roles and advances of microbiome research in psychiatry and discusses the philosophical and ethical implications of microbiome big data and microbiome-based interventions for psychiatric patients. We also investigate whether these issues are really “new,” or “old wine in new bottles.”. (shrink)

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. Increasing access to large datasets collected in real time could make certain mathematical models developed in research fields related to the management of urban systems obsolete. These ongoing evolutions are the subject of numerous works whose main angle of reflection is the future of cities rather than the transformations at work in the academic field. Our article proposes grasp the scientific dynamics (...) in areas of research related to two urban systems: transportation and water. The article demonstrates the importance of grasping these dynamics if we want to be able to apprehend what the urban management of tomorrow's cities will be like. To analyse these research areas’ dynamics, we use two complementary materials: bibliometric data and interviews. The interviews conducted in 2018 with academics and higher education officials in Paris and Edinburgh suggest avenues for hybridization between traditional modelling approaches and research in machine learning, artificial intelligence and Big Data. The bibliometric analysis highlight the trends at work: it shows that traffic flow as well as transportation studies are focussing more and more on AI and Big Data and that traffic flow studies are arousing a growing interest among computer scientists, while, so far, this interest is less pronounced in the water research area, and more especially regarding water quality. The differences observed between research on transportation and that on water confirm the multifaceted nature of the developments at work and encourage us to reject overly hasty and simplistic generalisations about the transformations underway. (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)

This study examines how visibility of a content moderator and ambiguity of moderated content influence perception of the moderation system in a social media environment. In the course of a two-day pre-registered experiment conducted in a realistic social media simulation, participants encountered moderated comments that were either unequivocally harsh or ambiguously worded, and the source of moderation was either unidentified, or attributed to other users or an automated system (AI). The results show that when comments were moderated by an AI (...) versus other users, users perceived less accountability in the moderation system and had less trust in the moderation decision, especially for ambiguously worded harassments, as opposed to clear harassment cases. However, no differences emerged in the perceived moderation fairness, objectivity, and participants confidence in their understanding of the moderation process. Overall, our study demonstrates that users tend to question the moderation decision and system more when an AI moderator is visible, which highlights the complexity of effectively managing the visibility of automatic content moderation in the social media environment. (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)

The diffusion of codes is an important processing technology for big data networks. In previous scheme, data analysis was conducted for small samples of big data and complex problems that cannot be processed by big data technology. Due to the limited capacity of intelligence device, a better method is to select a set of nodes to form a connected dominating set to save energy, and constructing CDS is proved to be a complete NP problem. However, it is a challenge to (...) reduce the communication delay and complexity for urgent data transmission in big data. In this paper, an appropriate duty cycle control scheme is proposed to reduce communication delay and complexity while improving energy efficient in CDS-based WSNs. In ADCC scheme, the method for constructing CDS is proposed at lower complexity. Nodes in CDS are selected according to the degree of nodes. Then, duty cycle of dominator nodes in CDS is higher than that of dominated nodes, so the communication delay in the proposed scheme is far less than that of previous scheme. The duty cycle of dominated nodes is small to save energy. This is because the number of dominator nodes in CDS is far less than the number of dominated nodes whose duty cycle is small; thus, the total energy consumption of the network is less than that of the previous scheme. As a result, the performance of energy consumption and communication delay and complex have been improved. Its complexity O∑i=0vm−i+2m−2v−s is reduced a lot for big data. The theoretical analysis shows that compared to the previous scheme, the transmission delay can be reduced 25–92% and the energy efficiency is improved by about 80% while retaining network lifetime. (shrink)

The massive amount of information brought about by the era of big data has enormous potential value. In-depth discussion and analysis of solving the information asymmetry between tax collection and taxpayers are keys. This paper provides an in-depth study and analysis of the fiscal and tax intelligent risk management strategies of listed companies in the big data environment. The tax risk management of listed companies is optimized. Tax authorities should follow the development trend of big data, apply big data (...) thinking, comprehensively excavate and utilize tax-related information, and improve the tax risk management model. At the same time, they should make full use of information technology to solve the problem of information asymmetry between tax collection and taxpayers and improve the quality and efficiency of tax risk management. The work discusses the problems of the current tax collection and management model in terms of management thinking, information collection, data analysis, legal system, and talent technology, and analyses the challenges brought by big data technology to the tax collection and management model as well as tax source management and tax informatization. (shrink)

BackgroundThe experiences of UK ethnic minority healthcare workers are crucial to ameliorating the disproportionate COVID-19 infection rate and outcomes in the UKEM community. We conducted a qualitative study on UKEM healthcare workers’ perspectives on COVID-19 vaccine hesitancy in the UKEM community.MethodsParticipants were 15 UKEM healthcare workers. Data were collected using individual and joint interviews, and a focus group, and analyzed using thematic analysis.ResultsWe generated three themes: heterogeneity, mistrust, and mitigating. Therein, participants distinguished CVH in the UKEM community in educational attainment (...) and ethnicity. They pointed to the role of mistrust in CVH in the UKEM community. They opined that the mistrust underlying CVH in the UKEM community is rooted in history and religion, conspiracy theories, the speedy development and novelty of the vaccines, post-vaccination complications/side effects, false positive test results, and social media and social support/influence. Participants recommended that interventions targeted at mitigating CVH in the UKEM community need to, in a non-judgmental way, tackle dis/misinformation and provide education, and incorporate UKEM healthcare worker endorsement. They also suggested such interventions be community-oriented, enhance the convenience of vaccination centers and the possibility of vaccine choice, and appreciate that overcoming CVH and accepting vaccination is a gradual process involving personal assessment of risks and benefits.ConclusionCVH in the UKEM community is a multifaceted phenomenon requiring multicomponent interventions. (shrink)

Data protection law has a linear logic, in that it purports to trace the lifecycle of personal data from creation to collection, processing, transfer, and ultimately its demise, and to regulate each step so as to promote the data subject’s control thereof. Big data defies this linear logic, in that it decontextualizes data from its original environment and conducts an algorithmic nonlinear mix, match, and mine analysis. Applying data protection law to the processing of big data does not work (...) well, to say the least. This Article examines the case of big medical data. A survey of emerging research practices indicates that studies either ignore data protection law altogether or assume an ex post position, namely that because they are conducted after the data has already been created in the course of providing medical care, and they use de-identified data, they go under the radar of data protection law. These studies focus on the end-point of the lifecycle of big data: if sufficiently anonymous at publication, the previous steps are overlooked, on the claim that they enjoy immunity. I argue that this answer is too crude. To portray data protection law in its best light, we should view it as a process-based attempt to equip data subjects with some power to control personal data about them, in all phases of data processing. Such control reflects the underlying justification of data protection law as an implementation of human dignity. The process-based approach fits current legal practices and is justified by reflecting dignitarian conceptions of informational privacy. (shrink)

Children continue to be overlooked as a topic of concern in discussions around the ethical use of people’s data and information. Where children are the subject of such discussions, the focus is often primarily on privacy concerns and consent relating to the use of their data. This paper highlights the unique challenges children face when it comes to online interferences with their decision-making, primarily due to their vulnerability, impressionability, the increased likelihood of disclosing personal information online, and their developmental capacities. (...) These traits allow for practices such as hypernudging to be executed on them more accurately and with more serious consequences, specifically by potentially undermining their autonomy. We argue that children are autonomous agents in the making and thus require additional special protections to ensure that the development of their autonomy is safeguarded. This means that measures should be taken to prohibit most forms of hypernudging children and thus ensure that they are protected from this powerful technique of digital manipulation. (shrink)

To study the static software defect detection system, based on the traditional static software defect detection system design, a new static software defect detection system design based on big data technology is proposed. The proposed method can optimize the distribution of test resources and improve the quality of software products by predicting the potential defect program modules and design the software and hardware of the static software defect detection system of big data technology. It is found that the traditional static (...) software defect detection system design based on code source data takes a long time, averaging 65 h /day. However, the traditional static software defect detection system based on deep learning has a short detection time, averaging 35 h/day. In this article, the detection time of the static software defect detection system based on big data is shorter than that of the other two traditional system designs, with an average of 15 h/day. Because the system design adjusts the operating state of the system, it improves the accuracy of data operation. On the premise of data collection, the system inspection research is completed, which ensures the operational safety of software data, alleviates the contradiction between system and data to a high degree, improves the efficiency of system operation, reduces unnecessary operations, further shortens the time required for inspection, improves the system performance, and has higher research and operation value. (shrink)

The conceptualisation of schooling is often based on “ideal children” in “ideal situations.” However, in determining the level of participation for children who are considered vulnerable in schooling, it is important to understand the lived experiences of these children. In this study, migrant children (particularly undocumented ones) in South Africa are the focus, and their lived experiences were considered through reflections from their parents and teachers. Data were collected using semi-structured interviews, and analysed using a constant comparative method of qualitative (...) analysis within a grounded theory approach. The study found that challenges affecting migrant children’s schooling include the lack of documentation, language barriers, issues of transition and adaptation (discrimination), and the inability to access further education. Strategies were identified to address the challenges, including schools liaising with the Department of Home Affairs, implementing cultural diversity programmes within the school, and through deliberate inclusive programmes. (shrink)

Children continue to be overlooked as a topic of concern in discussions around the ethical use of people’s data and information. Where children are the subject of such discussions, the focus is often primarily on privacy concerns and consent relating to the use of their data. This paper highlights the unique challenges children face when it comes to online interferences with their decision-making, primarily due to their vulnerability, impressionability, the increased likelihood of disclosing personal information online, and their developmental capacities. (...) These traits allow for practices such as hypernudging to be executed on them more accurately and with more serious consequences, specifically by potentially undermining their autonomy. We argue that children are autonomous agents in the making and thus require additional special protections to ensure that the development of their autonomy is safeguarded. This means that measures should be taken to prohibit most forms of hypernudging children and thus ensure that they are protected from this powerful technique of digital manipulation. (shrink)

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or (...) treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

The study investigated postgraduate students’ knowledge of plagiarism, forms of plagiarism they commit, the reasons they commit plagiarism and actions taken against postgraduate students who plagiarise at Mzuzu University in Malawi. The study adopted a mixed methods approach. The quantitative data were collected by distributing questionnaires to postgraduate students and academic staff whereas qualitative data were collected by conducting follow-up interviews with some academics, an assistant registrar and assistant librarian. The study found that despite students reporting that they had a (...) conceptual understanding of plagiarism, the majority of them reported that they had intentionally and unintentionally committed plagiarism, mainly due to pressure for good grades (86.7%), laziness and poor time management (84.9%), and lack of good academic writing skills (84.9%). The study also established that prevalent forms of plagiarism admitted (by students) and reported (by academic staff) to have been committed included lack of proper acknowledgement after paraphrasing (69.8%), summarising (64.1%) and using quotation marks (56.6%). The study further found that the common sanctions applied by academics include giving a warning and asking the student to re-write the plagiarised work. The study recommends that Mzuzu University should carry out awareness campaigns about the negative effects of plagiarism, targeting postgraduate students; and should introduce advanced academic writing skills training for postgraduate students. (shrink)

BackgroundAdvances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementation of AI could mean such corporations, clinics and public bodies will have a greater than typical role in obtaining, utilizing and protecting patient health information. This raises privacy issues relating to implementation and data security. Main bodyThe first set of concerns includes access, use and control (...) of patient data in private hands. Some recent public–private partnerships for implementing AI have resulted in poor protection of privacy. As such, there have been calls for greater systemic oversight of big data health research. Appropriate safeguards must be in place to maintain privacy and patient agency. Private custodians of data can be impacted by competing goals and should be structurally encouraged to ensure data protection and to deter alternative use thereof. Another set of concerns relates to the external risk of privacy breaches through AI-driven methods. The ability to deidentify or anonymize patient health data may be compromised or even nullified in light of new algorithms that have successfully reidentified such data. This could increase the risk to patient data under private custodianship.ConclusionsWe are currently in a familiar situation in which regulation and oversight risk falling behind the technologies they govern. Regulation should emphasize patient agency and consent, and should encourage increasingly sophisticated methods of data anonymization and protection. (shrink)

The emergence of personal assistants in the form of smart speakers has begun to significantly alter people’s everyday experiences with technology. The rate at which household Intelligent Personal Assistants such as Amazon’s Echo and Google Home emerged in household spaces has been rapid. They have begun to move human–computer interaction from text-based to voice-activated input, offering a multiplicity of features through speech. The supporting infrastructure connects with artificial intelligence and the internet of things, allowing digital interfaces with domestic appliances, lighting (...) systems, thermostats, media devices and more. Yet this also constitutes a significant new production of situated and sensitive data. This study focuses on how users negotiate and make choices about household Intelligent Personal Assistant use in connection with their data. This study is based on empirical research in Europe with early adopters in Germany and potential users in the Netherlands. This examination of users’ early stage technology acceptance considerations highlights particular practices and choices of users to either preserve their privacy or determine what is acceptable use for their data. Drawing on a simplified version of Unified Theory of Acceptance and Use of Technology 2, a quantitative model for technology acceptance, we demonstrate how acceptance of a household Intelligent Personal Assistants does not imply access to all household data, how users see usefulness in relation to a proliferation of devices, and note the recognition by users regarding the efforts needed for full use and acceptance. The study highlights the complexity of data production at a household level and how these devices produce myopic views of users for platforms. (shrink)

BackgroundIt has been argued that ethics review committees—e.g., Research Ethics Committees, Institutional Review Boards, etc.— have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data.Main bodyFocusing on the example of medical research databases we highlight here ethical issues around de-identified data sharing which motivate the (...) need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work.ConclusionData access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise. (shrink)