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)

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)

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)

The paper outlines critical aspects concerning the increasing use of big data in agriculture and farming. In particular, the aim is to shed light on the emerging dominance of the platform economy in the field of agriculture and food production. To analyze those power structures shaping this dynamic, we start with brief observations on the general relationship between digitization and agriculture and explain the platform economy, its general business model, and the proprietary forms of market power emerging from it. Subsequently, (...) we analyze the implications of the involvement of platform-economic companies in the agricultural sector. To do so, we critically examine several key examples that illuminate the strategies and ultimate objectives of those key tech players involved in this transition––particularly, two of the most dominant cloud providers, Alphabet and Amazon. Finally, we identify the main problems that can potentially emerge, and that can already be observed, from Big Tech’s involvement in agriculture and food production: (1) epistemic asymmetry (2) the erosion of privacy (3) potential harms to smaller farms and farmers, and (4) systemic dependencies on a monopolist data infrastructure. As we argue, these four dimensions are not only essential to take into account, but they also ultimately need to be responded to politically. From a policy perspective, to maintain data justice and epistemic symmetry, and to avoid the risks highlighted, we suggest––next to stressing the need for critical scholarship––to establish a democratically governed data trust, rely on open source technology, and fundamentally commit to the principle of collective data sovereignty. -/- Keywords: big tech, data sovereignty, digital farming, digital agriculture, surveillance capitalism, platform economy. (shrink)

Nowadays, the economic and social nature of contemporary business organizations chiefly banks binds them to face with the sheer volume of data and information and the key to commercial success in this area is the proper use of data for making better, faster and flawless decisions. To achieve this goal organizations requires strong and effective tools to enable them in automating task analysis, decision-making, strategy formulation and risk prediction to prevent bankruptcy and fraud .Business Intelligence is a set of skills, (...) technologies and application systems used to collect, store, analyze and create effective access to the task to help organizations better understand the business context and make accurate decision timely and respond quickly toward inflation, rate fluctuations and the market price. In this paper we review recent literature in the search for trends in business intelligence applications for the banking industry and its challenges and finally some articles that comprise this special issue are introduced and characterized in terms of business intelligence research framework. (shrink)

A subdiscipline has emerged around AI ethics, which is comprised of a wide array of individuals: computer scientists, ethicists, cognitive scientists, roboticists, legal professionals, economists, sociologists, gender, and race theorists. This has led to a very interesting branch of research, addressing issues surrounding the development and use of AI. This chapter will give a very brief snapshot of some of the most pertinent ethical concerns. Many of the issues in the Big Data Ethics chapter in this collection are often (...) applicable to AI ethics, because of the data that these technologies retrieve, store, and use, so will not be duplicated here.1 -/- While data-related issues are not new or unique to AI, AI does hold the potential to dramatically retrieve and analyze data that would not be possible otherwise. Data is being used in unique and transformative ways, such as the use of facial recognition to identify individuals from photos or CCTV; AI robots to retrieve live video feed about the patient(s) that it is monitoring; or in new ways, such as self-driving cars collecting an abundance of data about our surroundings, how we drive, and passengers in the car. There is the potential to infringe on individuals’ privacy, restriction of resources, or at worst, the creation of a surveillance society. (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)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium (...) is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

The Internet of Things (IoT) wireless LAN in healthcare has moved away from traditional methods that include hospital visits and continuous monitoring. The Internet of Things allows the use of certain means, including the detection, processing and transmission of physical and biomedical parameters. With powerful algorithms and intelligent systems, it will be available to provide unprecedented levels of critical data for real-time life that are collected and analyzed to guide people in research, management and emergency care. This chapter provides a (...) quick overview of IoT features and how they relate to wireless discovery and technology to deploy the medical applications you need. In the world, the revolution in any industry is to connect your products and devices to the Internet and make them independent and remotely connected, so that anyone can use and view them from anywhere and anytime. The Internet of Things provides us with a home automation system that uses smart devices to overcome this obstacle, allowing us to easily manage our appliances. A smart city is a vision to integrate a variety of information and communication solutions for residents with essential services, such as smart parking on all streets. The main motivation for using the Internet for parking objects is simply collecting data to get free parking. The IoT-based RTSSPS architecture is divided into three parts: a WSN-based smart street parking module, an IoT-based smart street parking module, and an IoT-based smart street parking module. IoT-based cloud with street parking algorithm, rating and future directions. (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)

Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction; it considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine thought experiment. Second, transient existence responds (...) to privacy’s loss by ceaselessly generating new personal identities, which translates into constantly producing temporarily unviolated private information. The ethics is explored through Gilles Deleuze’s metaphysics of difference applied in linguistic terms to the formation of the self. Comparing the exposure and transience alternatives leads to the conclusion that today’s big data reality splits the traditional ethical link between authenticity and freedom. Exposure provides authenticity, but negates human freedom. Transience provides freedom, but disdains authenticity. (shrink)

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

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will have to be (...) made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (shrink)

This literature review explores the issues and risks in non-conscious data collection and evaluates people’s attitudes towards it. In the modern world, data is one of the most valuable resources, yet studies focused on the potential negative implications of the new data-driven technologies are lacking. Therefore, this thesis conducts a comprehensive literature review to identify and assess risks in non-conscious data collection technologies that are most relevant and referenced in current literature. Accordingly, the most prominent risks are related (...) to privacy issues with personal data, bias in algorithms creating inequality, and the difficulties in creating adequate legislation. Subsequently, the thesis will explore existing studies about people’s attitudes towards non-conscious data collection and examine the most significant socio-demographic determinants of those attitudes. Therefore, despite socio-demographic factors such as age, gender, and economic status affecting the attitudes people have towards data collection technologies, this thesis argues how these approaches can be explained by varying external factors such as prior experiences with similar technologies. (shrink)

The background to this paper is that in our world of massively increasing personal digital data any control over the data about me seems illusionary – informational privacy seems a lost cause. On the other hand, the production of this digital data seems a necessary component of our present life in the industrialized world. A framework for a resolution of this apparent dilemma is provided if by the distinction between (meaningless) data and (meaningful) information. I argue that computational data processing (...) is necessary for many present-day processes and not a breach of privacy, while collection and processing of private information is often not necessary and a breach of privacy. The problem and the sketch of its solution are illustrated in a case-study: supermarket customer cards. (shrink)

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

There is increasing concern about “surveillance capitalism,” whereby for-profit companies generate value from data, while individuals are unable to resist (Zuboff 2019). Non-profits using data-enabled surveillance receive less attention. Higher education institutions (HEIs) have embraced data analytics, but the wide latitude that private, profit-oriented enterprises have to collect data is inappropriate. HEIs have a fiduciary relationship to students, not a narrowly transactional one (see Jones et al, forthcoming). They are responsible for facets of student life beyond education. In addition to (...) classrooms, learning management systems, and libraries, HEIs manage dormitories, gyms, dining halls, health facilities, career advising, police departments, and student employment. HEIs collect and use student data in all of these domains, ostensibly to understand learner behaviors and contexts, improve learning outcomes, and increase institutional efficiency through “learning analytics” (LA). ID card swipes and Wi-Fi log-ins can track student location, class attendance, use of campus facilities, eating habits, and friend groups. Course management systems capture how students interact with readings, video lectures, and discussion boards. Application materials provide demographic information. These data are used to identify students needing support, predict enrollment demands, and target recruiting efforts. These are laudable aims. However, current LA practices may be inconsistent with HEIs’ fiduciary responsibilities. HEIs often justify LA as advancing student interests, but some projects advance primarily organizational welfare and institutional interests. Moreover, LA advances a narrow conception of student interests while discounting privacy and autonomy. Students are generally unaware of the information collected, do not provide meaningful consent, and express discomfort and resigned acceptance about HEI data practices, especially for non-academic data (see Jones et al. forthcoming). The breadth and depth of student information available, combined with their fiduciary responsibility, create a duty that HEIs exercise substantial restraint and rigorous evaluation in data collection and use. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results (...) in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

The article introduces the concept of "surveillance-capitalist biopolitics" to problematize the recent expansion of "data extractivism" in health care and health research. As we show, this trend has accelerated during the ongoing Covid pandemic and points to a normalization and institutionalization of self-tracking practices, which, drawing on the "quantified self", points to the emergence of a "quantified collective". Referring to Foucault and Zuboff, and by analyzing key examples of the leading "Big Tech" companies (e.g., Alphabet and Apple), we argue that (...) contemporary forms of digital biopolitics are privatized, opaque, flexible, and not limited to the state. Instead, especially through the integration of wearable technologies, the biopolitical regulation of bodies is increasingly mediated by private tech companies. These companies rely on a questionable narrative of participation, responsibility, and care despite owning, and ultimately controlling, access to intimate health data and the proprietary algorithms mediating this data. The article shows that the proliferation of "surveillance-capitalist biopolitics" ultimately strengthens not only market power but also the epistemic and infrastructural power of the dataowning and gadget-producing firms. Finally, against an exclusively repressive and negative reading of biopolitics, and to effectively counter the forms of power emerging from surveillance-capitalist biopolitics, we propose four dimensions that are central to its democratizationnamely privacy/individual sovereignty, democratic deliberation, pluralism, and epistemic equality. -/- DOI: 10.1007/s41358-021-00309-9. (shrink)

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

Throughout the 20th century, market capitalism was defended on parallel grounds. First, it promotes freedom by enabling individuals to exploit their own property and labor-power; second, it facilitates an efficient allocation and use of resources. Recently, however, both defenses have begun to unravel—as capitalism has moved into its “platform” phase. Today, the pursuit of allocative efficiency, bolstered by pervasive data surveillance, often undermines individual freedom rather than promoting it. And more fundamentally, the very idea that markets are necessary to achieve (...) allocative efficiency has come under strain. Even supposing, for argument’s sake, that the claim was true in the early 20th century when von Mises and Hayek pioneered it, advances in computing have rekindled the old “socialist calculation” debate. And this time around, markets—as information technology—are unlikely to have the upper hand. -/- All of this, we argue, raises an important set of governance questions regarding the political economy of the future. We focus on two: How much should our economic system prioritize freedom, and to what extent should it rely on markets? The arc of platform capitalism bends, increasingly, toward a system that neither prioritizes freedom nor relies on markets. And the dominant critical response, exemplified by Shoshana Zuboff’s work, has been to call for a restoration of market capitalism. Longer term, however, we believe it would be more productive to think about how “postmarket” economic arrangements might promote freedom—or better yet, autonomy—even more effectively than markets, and to determine the practical steps necessary to realize that possibility. (shrink)

This review delves into the influence of big data analytics on project management effectiveness and project success rates. By examining applications, accomplishments, hindrances, and emerging developments in the context of big data analytics and project management, this review provides insights into its transformative potential. Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, privacy, and project manager training remain to be addressed. This review underscores the value (...) of data-driven decision-making for both practitioners and researchers in the project management field. (shrink)

There is a long-running debate as to whether privacy is a matter of control or access. This has become more important following revelations made by Edward Snowden in 2013 regarding the collection of vast swathes of data from the Internet by signals intelligence agencies such as NSA and GCHQ. The nature of this collection is such that if the control account is correct then there has been a significant invasion of people's privacy. If, though, the access account is (...) correct then there has not been an invasion of privacy on the scale suggested by the control account. I argue that the control account of privacy is mistaken. However, the consequences of this are not that the seizing control of personal information is unproblematic. I argue that the control account, while mistaken, seems plausible for two reasons. The first is that a loss of control over my information entails harm to the rights and interests that privacy protects. The second is that a loss of control over my information increases the risk that my information will be accessed and that my privacy will be violated. Seizing control of another's information is therefore harmful, even though it may not entail a violation of privacy. Indeed, seizing control of another's information may be more harmful than actually violating their privacy. (shrink)

Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components. DOI: 10.13140/RG.2.2.12784.00004 .

One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant threat (...) to the legitimacy of such processes. Modelling my argument on Estlund’s threat of epistocracy, I call this the ‘threat of algocracy’. The article clarifies the nature of this threat and addresses two possible solutions. It is argued that neither solution is likely to be successful, at least not without risking many other things we value about social decision-making. The result is a somewhat pessimistic conclusion in which we confront the possibility that we are creating decision-making processes that constrain and limit opportunities for human participation. (shrink)

I develop a contractualist theory of just intelligence collection based on the collective moral responsibility to deliver security to a community and use the theory to justify certain kinds of signals interception. I also consider the rights of various intelligence targets like intelligence officers, service personnel, government employees, militants, and family members of all of these groups in order to consider how targets' waivers or forfeitures might create the moral space for just surveillance. Even people who are not doing (...) anything prejudicial to other states' security can be modeled as ceding rights against diagnostic collection--light touch collection limited to ascertaining if someone is likely an intelligence threat--as part of their duty to support just foreign institutions. Foreign intelligence officers could not perform the same job potential targets can reasonably demand their intelligence agencies perform on their behalf without this ceding of rights. More invasive forms of collection can only be justified if officers can reasonably model their citizens consenting to being the target of the same sort of collection by adversary states. (shrink)

Le terme big data désigne l'extraction, la manipulation et l'analyse des ensembles de données trop volumineux pour être traités de manière routinière. Pour cette raison, des logiciels spéciaux sont utilisés et, dans de nombreux cas, des ordinateurs et du matériel informatiques dédiés. Généralement, ces données sont analysées de manière statistique. Les données doivent être traitées avec des outils de collecte et d'analyse avancés, basés sur des algorithmes prédéterminés, afin d'obtenir des informations pertinentes. Les algorithmes doivent également prendre en compte les (...) aspects invisibles pour les perceptions directes. DOI: 10.13140/RG.2.2.19453.51684 . (shrink)

In this article, we show how it is possible to lawfully buy an election. The method we describe for buying an election is novel. The key things that make it possible to buy an election are the existence of public voter registration lists where one can see whether a given elector has voted in a particular election, and the existence of Big Data Analytics that with a high degree of accuracy can predict what a given elector will vote in an (...) upcoming election. Someone interested in buying an election can enter an employment contract with all, or some of, the opponent electors where these electors are paid to do a job that prevents them from voting. By purchasing access to the public voter registration lists, it is possible to verify ex-post whether the opponent electors that one has signed a contract with have abstained. In the last two sections of the article, we discuss several barriers that can undermine an attempt to buy an election in the manner we identify. (shrink)

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

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data – the so-called ‘Big Data’ – can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasi-false and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as a total structure, or (...) as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. (shrink)

What is the epistemological status of Big Data? Is there really place for them in a scientific search for new empirical laws?

The use of Big Data presents significant legal problems, especially in terms of data protection. The existing legal framework of the European Union based in particular on the Directive no. 46/95/EC and the General Regulation on the Protection of Personal Data provide adequate protection. But for Big Data, a comprehensive and global strategy is needed. The evolution over time was from the right to exclude others to the right to control their own data and, at present, to the rethinking of (...) the right to (digital) identity. The General Data Protection Regulation, "GDPR" (Regulation EU 2016/679) deals with data protection and privacy of persons in the European Union and the European Economic Area. DOI: 10.13140/RG.2.2.29614.46400. (shrink)

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

What does Artificial Intelligence (AI) have to contribute to health care? And what should we be looking out for if we are worried about its risks? In this paper we offer a survey, and initial evaluation, of hopes and fears about the applications of artificial intelligence in medicine. AI clearly has enormous potential as a research tool, in genomics and public health especially, as well as a diagnostic aid. It’s also highly likely to impact on the organisational and business practices (...) of healthcare systems in ways that are perhaps under-appreciated. Enthusiasts for AI have held out the prospect that it will free physicians up to spend more time attending to what really matters to them and their patients. We will argue that this claim depends upon implausible assumptions about the institutional and economic imperatives operating in contemporary healthcare settings. We will also highlight important concerns about privacy, surveillance, and bias in big data, as well as the risks of over trust in machines, the challenges of transparency, the deskilling of healthcare practitioners, the way AI reframes healthcare, and the implications of AI for the distribution of power in healthcare institutions. We will suggest that two questions, in particular, are deserving of further attention from philosophers and bioethicists. What does care look like when one is dealing with data as much as people? And, what weight should we give to the advice of machines in our own deliberations about medical decisions? (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...) of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

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

In this chapter I defend the construction of inclusive, tightly governed DNA databases, as long as police can access them only for the prosecution of the most serious crimes or less serious but very high-volume offences. I deny that that the ethics of collecting and using these data sets the pattern for other kinds of policing by big data, notably predictive policing. DNA databases are primarily used for matching newly gathered biometric data with stored data. After considering and disputing a (...) number of objections to this practice, I conclude that DNA databases used in this way are ethically acceptable, if not valuable, contributions to legitimate policing. (shrink)

In the last half century we have gone from storing data on 5-1/4 inch floppy diskettes to cloud and now fog computing. But one should ask why so much data is being collected. Part of the answer is simple in light of scientific projects but why is there so much data on us? Then, we ask about its “interface” through fog computing. Such questions prompt this chapter on the philosophy of big data and fog computing. After some background on definitions, (...) origins and contemporary applications, the main discussion begins with thinking about modern data collection, management, and applications from a complexity standpoint. Big data is turned into knowledge but knowledge is extrapolated from the past and used to manage the future. Yet it is questionable whether humans have the capacity to manage contemporary technological and social complexity evidenced by our world in crisis and possibly on the brink of extinction. Such calls for a new way of studying societies from a scientific point of view. We are at the center of the observation from which big data emerge and are manipulated, the overall human project being not only to create an artificial brain with an attendant mind but a society that might be able to survive what “natural” humans cannot. (shrink)

In 1990, Gilles Deleuze publishedPostscript on the Societies of Control, an introduction to the potentially suffocating reality of the nascent control society. This thirty-year update details how Deleuze’s conception has developed from a broad speculative vision into specific economic mechanisms clustering around personal information, big data, predictive analytics, and marketing. The central claim is that today’s advancing control society coerces without prohibitions, and through incentives that are not grim but enjoyable, even euphoric because they compel individuals to obey their own (...) personal information. The article concludes by delineating two strategies for living that are as unexplored as control society itself because they are revealed and then enabled by the particular method of oppression that is control. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering the roles (...) of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

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

Abstract: The field of life extension is full of ideas but they are unstructured. Here we suggest a comprehensive strategy for reaching personal immortality based on the idea of multilevel defense, where the next life-preserving plan is implemented if the previous one fails, but all plans need to be prepared simultaneously in advance. The first plan, plan A, is the surviving until advanced AI creation via fighting aging and other causes of death and extending one’s life. Plan B is cryonics, (...) which starts if plan A fails, and assumes cryopreservation of the brain until technical capabilities to return it to life appear. Plan C is digital immortality in the sense of collecting data about the person now so future AI will be able to recreate a model of a person. Plan D is the hope based on some unlikely scenarios of infinite survival, like so-called “quantum immortality”. All these plans have personal and social perspective. The personal aspect means efforts of the increasing chances of personal survival via taking care about one’s own health, signing cryocontract or collecting digital immortality data. The social aspect means the participation in collective work towards creation and increase of the availability of life extension technologies, which includes funding scientific research, promotion of life extension value and direct performing of research and implementation, as well as preventing global catastrophic risk. All plans converge at the end, as their result is the indefinite survival as an uploaded mind inside an ecosystem, created by a superintelligent AI. (shrink)

Technological advances are bringing new light to privacy issues and changing the reasons for why privacy is important. These advances have changed not only the kind of personal data that is available to be collected, but also how that personal data can be used by those who have access to it. We are particularly concerned with how information about personal attributes inferred from collected data (such as online behaviour), can be used to tailor messages and services to specific individuals or (...) groups. This kind of ‘personalised targeting’ has the potential to influence individuals’ perceptions, attitudes, and choices in unprecedented ways. In this paper, we argue that because it is becoming easier for companies to use collected data for influence, threats to privacy are increasingly also threats to personal autonomy—an individual’s ability to reflect on and decide freely about their values, actions, and behaviour, and to act on those choices.4 While increasing attention is directed to the ethics of how personal data is collected, we make the case that a new ethics of privacy needs to also think more rigorously about how personal data may be used, and its potential impact on personal autonomy. (shrink)

Disputes at the intersection of national security, surveillance, civil liberties, and transparency are nothing new, but they have become a particularly prominent part of public discourse in the years since the attacks on the World Trade Center in September 2001. This is in part due to the dramatic nature of those attacks, in part based on significant legal developments after the attacks (classifying persons as “enemy combatants” outside the scope of traditional Geneva protections, legal memos by White House counsel providing (...) rationale for torture, the USA Patriot Act), and in part because of the rapid development of communications and computing technologies that enable both greater connectivity among people and the greater ability to collect information about those connections. -/- One important way in which these questions intersect is in the controversy surrounding bulk collection of telephone metadata by the U.S. National Security Agency. The bulk metadata program (the “metadata program” or “program”) involved court orders under section 215 of the USA Patriot Act requiring telecommunications companies to provide records about all calls the companies handled and the creation of database that the NSA could search. The program was revealed to the general public in June 2013 as part of the large document leak by Edward Snowden, a former contractor for the NSA. -/- A fair amount has been written about section 215 and the bulk metadata program. Much of the commentary has focused on three discrete issues. First is whether the program is legal; that is, does the program comport with the language of the statute and is it consistent with Fourth Amendment protections against unreasonable searches and seizures? Second is whether the program infringes privacy rights; that is, does bulk metadata collection diminish individual privacy in a way that rises to the level that it infringes persons’ rights to privacy? Third is whether the secrecy of the program is inconsistent with democratic accountability. After all, people in the general public only became aware of the metadata program via the Snowden leaks; absent those leaks, there would have not likely been the sort of political backlash and investigation necessary to provide some kind of accountability. -/- In this chapter I argue that we need to look at these not as discrete questions, but as intersecting ones. The metadata program is not simply a legal problem (though it is one); it is not simply a privacy problem (though it is one); and it is not simply a secrecy problem (though it is one). Instead, the importance of the metadata program is the way in which these problems intersect and reinforce one another. Specifically, I will argue that the intersection of the questions undermines the value of rights, and that this is a deeper and more far-reaching moral problem than each of the component questions. (shrink)

In the end of the course the student will be able to: • Understand the functioning of data mining tools and their contributions to managerial professions • Master the use of dynamic search tools on the open web and on the dark web. • Use the proper tools according to the objectives sought • Master the latest trends and innovations in Business Analytics • Analyze the opportunities offered in terms of data mining by artificial intelligence and IoT.

This study investigated Boko Haram and terrorist activities in Nigeria while looking at the ethical implications and responses of the Christians. The study was guided by two objectives which are to; analyse whether the acts of terror carried out by Boko Haram are ethical and examine the responses of the Christians with respect to Boko Haram acts of terror. However, the methods of exposition and critical analysis was used and content analysis was used to analyse data collected. Data was collected (...) from secondary sources like textbooks, journal articles, interviews, institutional materials and internet sources. The study also looked at concept of terrorism and a brief history of Boko Haram. The findings of the study showed that since Boko Haram acts of terrorism is simply a display of violence to terrify the populace and the government, it can be said that their actions and activities which include violence is unacceptable and could be termed wrong taking cognizance of the consequentialist view which opines that the morality of an action depends on its results. The ethics of care which is seen as a context-based approach to preserve relationship, negates Boko Haram acts of terror which apparently has a considerable effect on a number of relationships. The findings further showed that the Christians responded by rejecting to integrate with Muslims and also encouraged Christians to move to areas where there are major Christian settlements while others had to fall back to the natural tendency of self-preservation. (shrink)

Automated Influence is the use of Artificial Intelligence to collect, integrate, and analyse people’s data in order to deliver targeted interventions that shape their behaviour. We consider three central objections against Automated Influence, focusing on privacy, exploitation, and manipulation, showing in each case how a structural version of that objection has more purchase than its interactional counterpart. By rejecting the interactional focus of “AI Ethics” in favour of a more structural, political philosophy of AI, we show that the real problem (...) with Automated Influence is the crisis of legitimacy that it precipitates. (shrink)

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

This paper builds off the Our Data Ourselves research project, which examined ways of understanding and reclaiming the data that young people produce on smartphone devices. Here we explore the growing usage and centrality of mobiles in the lives of young people, questioning what data-making possibilities exist if users can either uncover and/or capture what data controllers such as Facebook monetize and share about themselves with third-parties. We outline the MobileMiner, an app we created to consider how gaining access to (...) one’s own data not only augments the agency of the individual but of the collective user. Finally, we discuss the data making that transpired during our hackathon. Such interventions in the enclosed processes of datafication are meant as a preliminary investigation into the possibilities that arise when young people are given back the data which they are normally structurally precluded from accessing. (shrink)