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In this book, Seumas Miller develops distinctive philosophical analyses of corruption, collective responsibility and integrity systems, and applies them to cases in both the public and the private sectors. Using numerous well-known examples of institutional corruption, he explores a variety of actual and potential anti-corruption measures. The result is a wide-ranging, theoretically sophisticated and empirically informed work on institutional corruption and how to combat it. Part I defines the key concepts of corruption, power, collective responsibility, bribery, abuse of authority and nepotism; Part II discusses anti-corruption and integrity systems, corruption investigations and whistle-blowing; and Part III focuses on corruption and anti-corruption in specific institutional settings, namely policing, finance, business and government. Integrating theory with practical approaches, this book will be important for those interested in the philosophy and ethics of corruption as well as for those who work to combat it

High levels of police corruption have been a persistent historical tendency in police services throughout the world. While the general area of concern in this book is with police corruption and anti-corruption, the focus is on certain key philosophical and ethical issues that arise for police organisations confronting corruption. On the normative account proffered in this book the principal institutional purpose of policing is the protection of legally enshrined moral rights and the principal institutional anti-corruption arrangement is what is referred to as an integrity system. The latter includes oversight bodies with investigative powers and internal affairs departments as well as specific devices such as early warning indicators, professional reporting mechanisms and integrity tests. Key concepts analysed in the book include corruption, noble cause corruption and collective moral responsibility. The key ethical issues analysed include investigative independence, professional reporting, covert operations and integrity tests

Introduction -- Morally permissible use of lethal force: a taxonomy -- Killing in self-defence -- Police officers, regular soldiers and normative institutional analysis -- Police use of lethal force -- Police use of lethal force and suicide bombers -- Military use of lethal force -- Civilian immunity -- Humanitarian armed intervention -- Targeted killing -- Autonomous weapons and moral responsibility -- Conclusion

In this book, Seumas Miller examines the moral foundations of contemporary social institutions. Offering an original general theory of social institutions, he posits that all social institutions exist to realize various collective ends, indeed, to produce collective goods. He analyses key concepts such as collective responsibility and institutional corruption. Miller also provides distinctive special theories of particular institutions, including governments, welfare agencies, universities, police organizations, business corporations, and communications and information technology entities. These theories are philosophical and, thus, foundational and synoptic in character. They are normative accounts of a sampling of contemporary social institutions, not descriptive accounts of all social institutions, both past and present. Miller also addresses various ethical challenges confronting contemporary institutional designers and policymakers, including the renovation of the international financial system, the 'dumbing down' of the media, the challenge of world poverty, and human rights infringements by security agencies combating global terrorism

Undoubtedly, the events of September 11, 2001 served as a wake-up call to the scourge of global terrorism facing twenty-first century societies. But was the attack on the World Trade Center a crime or an act of war? Is seemingly indiscriminate violence inflicted on civilians ever morally justified? And should society's response always be in kind--with blind, destructive violence? For that matter, are all civilians truly "innocent"? The answers are not always so simple.Terrorism and Counter-Terrorism: Ethics and Liberal Democracy provides sobering analyses of the nature of terrorism and the mor

"Terrorism and Counter-Terrorism: Ethics and Liberal Democracy provides sobering analyses of the nature of terrorism and the moral justification - or lack thereof - of terrorist actions and counter-terrorism measures in today's world. Utilizing a variety of thought-provoking philosophical arguments, the historic roots of terrorism and its contemporary incarnations are explored in depth. Detailed analyses of organizations such as the IRA, the ANC, Hamas and Al-Qaeda reveal the many faces of terrorism and its disparate motives and tactics. Discussion of the nature and scope of terrorism and whether it can ever be morally justified is balanced with analysis of counter-terrorism strategies and the methods and moral limits of counter-terrorism."--Jacket

Social action is central to social thought. Avoiding both the excessively atomistic individualism of rational choice theorists and implausible collectivist assumptions, this provides philosophical analyses of fundamental categories of human social action, including cooperative action, conventional action, social norm governed action, and the actions of the occupants of organizational roles

Recent revelations concerning data firm Cambridge Analytica's illegitimate use of the data of millions of Facebook users highlights the ethical and, relatedly, legal issues arising from the use of machine learning techniques. Cambridge Analytica is, or was – the revelations brought about its demise - a firm that used machine learning processes to try to influence elections in the US and elsewhere by, for instance, targeting 'vulnerable' voters in marginal seats with political advertising. Of course, there is nothing new about political candidates and parties employing firms to engage in political advertising on their behalf, but if a data firm has access to the personal information of millions of voters, and is skilled in the use of machine learning techniques, then it can develop detailed, fine-grained voter profiles that enable political actors to reach a whole new level of manipulative influence over voters. My focus in this paper is not with the highly publicised ethical and legal issues arising from Cambridge Analytic's activities but rather with some important ethical issues arising from the use of machine learning techniques that have not received the attention and analysis that they deserve. I focus on three areas in which machine learning techniques are used or, it is claimed, should be used, and which give rise to problems at the interface of law and ethics (or law and morality, I use the terms "ethics" and "morality" interchangeably). The three areas are profiling and predictive policing (Saunders et al. 2016), legal adjudication (Zeleznikow, 2017), and machines' compliance with legally enshrined moral principles (Arkin 2010). I note that here, as elsewhere, new and emerging technologies are developing rapidly making it difficult to predict what might or might not be able to be achieved in the future. For this reason, I have adopted the conservative stance of restricting my ethical analysis to existing machine learning techniques and applications rather than those that are the object of speculation or even informed extrapolation (Mittelstadt et al. 2015). This has the consequence that what I might regard as a limitation of machine learning techniques, e.g. in respect of predicting novel outcomes or of accommodating moral principles, might be thought by others to be merely a limitation of currently available techniques. After all, has not the history of AI recently shown the naysayers to have been proved wrong? Certainly, AI has seen some impressive results, including the construction of computers that can defeat human experts in complex games, such as chess and Go (Silver et al. 2017), and others that can do a better job than human medical experts at identifying the malignancy of moles and the like (Esteva et al. 2017). However, since by definition future machine learning techniques and applications are not yet with us the general claim that current limitations will be overcome cannot at this time be confirmed or disconfirmed on the basis of empirical evidence.