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We present a model of organizational monitoring that integrates organizational justice and information privacy. Specifically, we adopt the position that the formation of invasiveness and unfairness attitudes is a goal-driven process. We employ cybernetic control theory and identity theory to describe how monitoring systems affect one's ability to maintain a positive self-concept. Monitoring provides a particularly powerful cue that directs attention to self-awareness. People draw on fairness and privacy relevant cues inherent in monitoring systems and embedded in monitoring environments (e.g., justice climate) to evaluate their identities. Discrepancies between actual and desired personal and social identities create distress, motivating employees to engage in behavioral self-regulation to counteract potentially threatening monitoring systems. Organizational threats to personal identity goals lead to increased invasiveness attitudes and a commitment to protect and enhance the self. Threats to social identity lead to increased unfairness attitudes and lowered commitment to one's organization. Implications for theory and research on monitoring, justice, and privacy are discussed along with practical implications.

Perpetrators of technology-facilitated gender-based violence are taking advantage of increasingly automated and sophisticated privacy-invasive tools to carry out their abuse. Whether this be monitoring movements through stalkerware, using drones to nonconsensually film or harass, or manipulating and distributing intimate images online such as deepfakes and creepshots, invasions of privacy have become a significant form of gender-based violence. Accordingly, our normative and legal concepts of privacy must evolve to counter the harms arising from this misuse of new technology. Canada's Supreme Court recently addressed technology-facilitated violations of privacy in the context of voyeurism in R v Jarvis (2019) . The discussion of privacy in this decision appears to be a good first step toward a more equitable conceptualization of privacy protection. Building on existing privacy theories, this chapter examines what the reasoning in Jarvis might mean for “reasonable expectations of privacy” in other areas of law, and how this concept might be interpreted in response to gender-based technology-facilitated violence. The authors argue the courts in Canada and elsewhere must take the analysis in Jarvis further to fully realize a notion of privacy that protects the autonomy, dignity, and liberty of all.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.

Reusing existing data sets of health information for public health or medical research has much to recommend it. Much data repurposing in medical or public health research or practice involves information that has been stripped of individual identifiers but some does not. In some cases, there may have been consent to the reuse but in other cases consent may be absent and people may be entirely unaware of how the data about them are being used. Data sets are also being combined and may contain information with very different sources, consent histories, and individual identifiers. Much of the ethical and policy discussion about the permissibility of data reuse has centered on two questions: for identifiable data, the scope of the original consent and whether the reuse is permissible in light of that scope, and for de-identified data, whether there are unacceptable risks that the data will be reidentified in a manner that is harmful to any data subjects. Prioritizing these questions rests on a picture of the ethics of data use as primarily about respecting the choices of the data subject. We contend that this picture is mistaken; data repurposing, especially when data sets are combined, raises novel questions about the impacts of research on groups and their implications for individuals regarded as falling within these groups. These impacts suggest that the controversies about de-identification or reconsent for reuse are to some extent beside the point. Serious ethical questions are also raised by the inferences that may be drawn about individuals from the research and resulting risks of stigmatization. These risks may arise even when individuals were not part of the original data set being repurposed. Data reuse, repurposing, and recombination may have damaging effects on others not included within the original data sets. These issues of justice for individuals who might be regarded as indirect subjects of research are not even raised by approaches that consider only the implications for or agreement of the original data subject. This chapter argues that health information should be available for reuse, information should be available for use, but in a way that does not yield unexpected surprises, produce direct harm to individuals, or violate warranted trust.

Upon reading this chapter, the reader will understand

• How consumers perceive a privacy loss when exposed to retailers' big data analytics

• The role played by the social environment in terms of the opinions of relatives and friends largely influence how youth perceive the risk of privacy loss

• What makes the information about retailers' usage of data not entirely accessible by consumers

• Consumers perception of retailers' usage of their data

How consumers perceive a privacy loss when exposed to retailers' big data analytics

The role played by the social environment in terms of the opinions of relatives and friends largely influence how youth perceive the risk of privacy loss

What makes the information about retailers' usage of data not entirely accessible by consumers

Consumers perception of retailers' usage of their data

Social networking platforms such as Facebook have infiltrated the lives of many students, and as such it is natural to consider how they can be effectively used to enhance learning. This chapter provides a comprehensive review of social networking in education from a design perspective. Social networking is defined based on Boyd & Ellison’s seminal definition of connected profiles, and is distinguished from social media for the purposes of investigation. Facebook, Edmodo, and other social networking platforms are briefly described, before summarizing the wide variety of social networking usage reported in the research literature. The various benefits of social networking in education are distilled from the literature, including their capacity to facilitate community building, collaboration, reflection, and expedient access to learning. Issues surrounding the educational use of social networking are also organized into themes, for instance privacy concerns, distraction, cyber-safety, and technical constraints. The implications of findings from the social networking literature are synthesized into learning design and implementation recommendations. The chapter concludes with a discussion of open questions and areas for further investigation.

The Nationwide Suspicious Activity Reporting Initiative (NSI) is the focal point of the Information Sharing Environment (ISE), a radical reformulation of policies governing government intelligence activities within US borders. In the wake of the September 11th attacks, long-standing informational norms for the production, use, and circulation of domestic intelligence records containing personal information are being replaced with far less restrictive norms, altering a status quo that had been in effect since mid-1970s. Although the NSI represents an unprecedented expansion of human resources dedicated to the collection and production of domestic intelligence, it is not well known in privacy advocacy community. This chapter considers these and other terms in the context of relevant US law and policy, including the Privacy Act of 1974, the E-Government Act of 2002, Executive Order 12333, and 28 CFR Part 23. In addition to describing the federal (ISE-SAR) standard, the chapter examines the critical role of guidance in the logic of suspicious activity report (SAR) production, and the problematic role finished ISE-SARs seem to play in the matrix of federal and state-level watch lists. The program, if not properly regulated, could pose a considerable threat to personal privacy and the life chances and self-determination of all US persons. The chapter considers this threat in terms of Nissenbaum's (2010) “contextual integrity,” a theory of context-relative informational norms.

The received wisdom underlying many guides to ethical research is that information is private, and research is consequently seen as a trespass on the private sphere. Privacy demands control; control requires consent; consent protects privacy. This is not wrong in every case, but it is over-generalised. The distorted perspective leads to some striking misinterpretations of the rights of research participants, and the duties of researchers. Privacy is not the same thing as data protection; consent is not adequate as a defence of privacy; seeking consent is not always required or appropriate. Beyond that, the misinterpretation can lead to conduct which is unethical, limiting the scope of research activity, obstructing the flow of information in a free society, and failing to recognise what researchers’ real duties are.

Professional ethics require researchers to disclose all risks to participants in their studies. Changes in the legal climate in the United States and new modes of surveillance and communication call into question the effectiveness of measures used by researchers to protect participants from the risks they now face. This paper explores existing and newly enhanced risks to participants in social movement studies and examines problems with confidentiality agreements and informed consent procedures, two avenues that scholars traditionally use to protect research participants. The utility of Certificates of Confidentiality and researcher privilege also are examined as means to safeguard the privacy and security of research participants. The conclusion raises larger issues about the accountability of scholars to their research participants and the nature of risk in today's changing political climate. These include how to weigh potential risks and benefits to social movements and activists who are studied, the consequences for scholarship if scholars avoid studying movements and activists that pose risks, and the need for scholars to collaborate with research participants to tailor ethical research practices and to use institutional resources to challenge threats to the privacy and integrity of the people and groups they study.

Virtual learning environments (VLE) have in the past 15 years radically changed the way learning objects are spread among students and the way learners communicate with teachers. In parallel, social networks are now widely used by users with all kinds of profiles. Among social networks, Facebook is the one offering the largest array of functions and possibilities to be used as an open platform for various kinds of applications. That is why, in this chapter, we provide an overview of the possible use of Facebook as a VLE supporting a Massive Open Online Course (MOOC). The authors distinguish generic functionalities that can be found in the three most popular xMOOC platforms as well as the relevant concepts needed for effective learning that are manipulated by such platforms. Then the authors evaluate how, within Facebook groups, features can be used to create a fully working MOOC environment. The authors distinguish notably class management, communication, collaboration, assessment, learning activity management and the possibilities for learning object management. Overall, the authors highlight that, except for the assessment, Facebook can support all of the functionalities required by a modern MOOC platform while offering a high social presence. While not vital for most MOOC followers, further developments can nevertheless be made to customise Facebook for ensuring this assessment functionality. The chapter can be used as guidance for an implementation of the social network as MOOC through the use of a Facebook group.