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"The Ethics of Surveillance: An Introduction systematically and comprehensively examines the ethical issues surrounding the concept of surveillance. Addressing important questions such as:Is it ever acceptable to spy on one's allies? To what degree should the state be able to intrude into its citizens' private lives in the name of security? Can corporate espionage ever be justified?What are the ethical issues surrounding big data?How far should a journalist go in pursuing information?Is it reasonable to expect a degree of privacy in public?Is it ever justifiable for a parent to read a child's diary?Featuring case studies throughout, this textbook provides a philosophical introduction to an incredibly topical issue studied by students within the fields of applied ethics, ethics of technology, privacy, security studies, politics, journalism and human geography."--Provided by publisher.

Abstract
Mass surveillance is a more real threat now than at any time in history. Digital communications and automated systems allow for the collection and processing of private information at a scale never seen before. Many argue that mass surveillance entails a significant loss of privacy. Others dispute that there is a loss of privacy if the information is only encountered by automated systems.
This paper argues that automated mass surveillance does not involve a significant loss of privacy. Through providing a definition of informational privacy as a matter of actual access of private information by one who can understand the meaning of that information, it follows that automated systems which lack understanding cannot of themselves diminish privacy. This is not to say that mass surveillance is unproblematic, though: it is deeply problematic. It is just that privacy is not the most significant of these problems.

This book draws from the use of modern surveillance technologies during the COVID-19 pandemic to explore a set of issues and challenges facing decision-makers and designers in times of emergency: how do we respond to emergencies in ways that are both consistent with democratic and community principles, and that are ethically justifiable?.

The Covid-19 pandemic is arguably the first international emergency of the twenty-first century. In order to respond to this emergency, countries and governments around the world were forced to engage in a range of actions and policies that would not otherwise have been permitted. Looking in particular at the use of surveillance technologies, this book examines the challenge of ethics in emergencies. What can states do to keep their populations safe, what can citizens expect of their governments, and when are those government actions unjustified? By looking at the use of surveillance in times of emergency, this book explores ethical, philosophical, political, and social concepts, challenges them, and offers a set of views on where those concepts may evolve into the future. As a global population, we will be faced with emergencies, and it is possible that these will also be global in their impact. The ethics of surveillance in times of emergency is both of its time, and ongoing; we must learn our lessons from the last emergency, to be prepared for the next ones.

What's wrong with targeted advertising in political campaigns? Are echo chambers a matter of genuine concern? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges.

What's wrong with targeted advertising in political campaigns? Are echo chambers a matter of genuine concern? How does data collection impact on trust in society? As decision-making becomes increasingly automated, how can decision-makers be held to account? This collection consider potential solutions to these challenges.

Law enforcement authorities (LEAs) have begun using artificial intelligence and predictive policing applications that are likely to raise ethical, data protection, social, political and economic issues. This paper describes application of a new scenario methodology for identifying issues that emerging technologies are likely to raise in a future six or seven years hence, but that deserve policymakers' attention now. It often takes policymakers that long to develop a new policy, consult with stakeholders and implement the policy. Thus, policymakers need a structured, but concise framework in order to understand the issues and their various implications. At the same time, they also prefer policies that have stakeholder support. These considerations led the University of Twente in the Netherlands and the UK's Trilateral Research to develop the scenario that follows. It is structured with several headings that policymakers need to consider in order to move toward a desired future and avoidance of an undesired future.

There is a widespread belief that machine learning tools can be used to improve decision-making in health and social care. At the same time, there are concerns that they pose threats to privacy and confidentiality. Policy makers therefore need to develop governance arrangements that balance benefits and risks associated with the new tools. This article traces the history of developments of information infrastructures for secondary uses of personal datasets, including routine reporting of activity and service planning, in health and social care. The developments provide broad context for a study of the governance implications of new tools for the analysis of health and social care datasets. We find that machine learning tools can increase the capacity to make inferences about the people represented in datasets, although the potential is limited by the poor quality of routine data, and the methods and results are difficult to explain to other stakeholders. We argue that current local governance arrangements are piecemeal, but at the same time reinforce centralisation of the capacity to make inferences about individuals and populations. They do not provide adequate oversight, or accountability to the patients and clients represented in datasets.