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This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Closed Circuit Television (CCTV) cameras are a prominent, if increasingly familiar, feature of urbanism. They symbolize the faith that spatial authorities place in technical interventions for the treatment of social problems. CCTV was principally introduced to sterilize municipalities, to govern conducts and to protect properties. Vast expenditure has been committed to these technologies without a clear sense of how precisely they influence things. CCTV cameras might appear inanimate, but Opening the Black Box shows them to be vital mediums within relational circulations of supervision. The book principally excavates the social relations entwining the everyday application of CCTV. It takes the reader on a journey from living beneath the camera, to working behind the lens. Attention focuses on the labour exerted by camera operators as they source and process distanced spectacles. These workers are paid to scan monitor screens in search of disorderly vistas, visualizing stimuli according to its perceived riskiness and/or allurement. But the projection of this gaze can draw an unsettling reflection. It can mean enduring behavioural extremities as an impotent witness. It can also entail making spontaneous decisions that determine the course of justice. Opening the Black Box, therefore, contemplates the seductive and traumatic dimensions of monitoring telemediated ‘riskscapes’ through the prism of camera circuitry. It probes the positioning of camera operators as ‘vicarious’ custodians of a precarious social order and engages their subjective experiences. It reveals the work of watching to be an ambiguous practice: as much about managing external disturbances on the street as managing internal disruptions in the self.
A scholarly analysis of the Turkish military in the 21st century by the Near East policy expert and author of What Went Wrong in Afghanistan. On July 15th, 2016, a faction within the Turkish Armed Forces attempted a coup d’état against sitting President Recep Tayyip Erdoğan. Though the attempt was unsuccessful, the TAF would never be the same. In Opening the Black Box, former Turkish military advisor Metin Gurcan offers a rare look inside the TAF to examine how it has evolved in the 21stcentury. With twenty years of experience inside the Turkish military, both on the field and in the corridors of the Turkish General Staff, as well as extensive academic research, Gurcan provides two detailed snapshots of the TAF: one before July 15thand one after. Offering a complete view of this complex institution, Gurcan offers scholarly perspectives on the TAF as a security organization, a social institution and, in the case of career officers, a profession. Gurcan also examines the evolution of civilian-military relations in Turkey over the last decade with a specific focus on the impact of the July 15 Military Uprising on institutional identity.
Nobody wants to fail. But in highly complex organizations, success can happen only when we confront our mistakes, learn from our own version of a black box, and create a climate where it’s safe to fail. We all have to endure failure from time to time, whether it’s underperforming at a job interview, flunking an exam, or losing a pickup basketball game. But for people working in safety-critical industries, getting it wrong can have deadly consequences. Consider the shocking fact that preventable medical error is the third-biggest killer in the United States, causing more than 400,000 deaths every year. More people die from mistakes made by doctors and hospitals than from traffic accidents. And most of those mistakes are never made public, because of malpractice settlements with nondisclosure clauses. For a dramatically different approach to failure, look at aviation. Every passenger aircraft in the world is equipped with an almost indestructible black box. Whenever there’s any sort of mishap, major or minor, the box is opened, the data is analyzed, and experts figure out exactly what went wrong. Then the facts are published and procedures are changed, so that the same mistakes won’t happen again. By applying this method in recent decades, the industry has created an astonishingly good safety record. Few of us put lives at risk in our daily work as surgeons and pilots do, but we all have a strong interest in avoiding predictable and preventable errors. So why don’t we all embrace the aviation approach to failure rather than the health-care approach? As Matthew Syed shows in this eye-opening book, the answer is rooted in human psychology and organizational culture. Syed argues that the most important determinant of success in any field is an acknowledgment of failure and a willingness to engage with it. Yet most of us are stuck in a relationship with failure that impedes progress, halts innovation, and damages our careers and personal lives. We rarely acknowledge or learn from failure—even though we often claim the opposite. We think we have 20/20 hindsight, but our vision is usually fuzzy. Syed draws on a wide range of sources—from anthropology and psychology to history and complexity theory—to explore the subtle but predictable patterns of human error and our defensive responses to error. He also shares fascinating stories of individuals and organizations that have successfully embraced a black box approach to improvement, such as David Beckham, the Mercedes F1 team, and Dropbox.
This book presents an overview of why implementation research has contributed to a major reconsideration of the process of policy formation and offers conceptual frameworks that employ implementation research to develop a fuller understanding of the entire policy process. The contributors caution the error of assuming that implementation is the main factor in policy making and that once implementation is taken care of, policies will be effective. They attempt to place implementation in the broader policymaking process and show its relationship to the other parts of the policy cycle. Additionally, several of the contributors develop explanatory models that cut across the research dichotomies of the prevailing top-down and bottom-up approaches and establish an agenda for future research.
A book full of boxes. A box in itself. An unboxing. This book explores boxes in their broadest sense and size. It invites us to step into the field, unravel how and why things are contained and how it might be otherwise. By turning the focus of Science and Technology Studies (STS) to boxing practices, this collation of essays examines boxes as world-making devices. Gathered in the format of a field guide, it offers an introduction to ways of ordering the world, unpacking their boxed-up, largely invisible politics and epistemics. Performatively, pushing against conventional uses of academic books, this volume is about rethinking taken-for-granted formats and infrastructures of scholarly ordering - thinking, writing, reading. It diverges from encyclopedic logics and representative overviews of boxing practices and the architectural organization of monographs and edited volumes through a single, overarching argument. This book asks its users to leave well-trodden paths of linear and comprehensive reading and invites them to read sideways, creating their own orders through associations and relating. Thus, this book is best understood as an intervention, a beginning, an open box, a slim volume that needs expansion and further experiments with ordering by its users.
Available Open Access digitally under CC-BY-NC-ND licence. Some of the largest quantities of data produced today occur as the result of experiments taking place at Big Science facilities. This book tells the story of a unique research journey following the people responsible for designing and implementing data management at a new Big Science facility, the European Spallation Source (ESS) in Lund, Sweden. It critically examines the idea of data as an absolute ‘truth’ and sheds light on the often underestimated, yet essential, contributions of these data experts. Providing a unique glimpse into the inner workings of Big Science, this book fills an important gap in science and technology studies and critical data studies.
For years, technologists and computer scientists have promised an AI revolution that would transform the very basis of how we imagine and administer modern medicine. AI-driven advancements in medical error rates, diagnostic accuracy, or disease outbreak detection could potentially save thousands of lives. But health AI also carries the potential for exacerbating deep systemic biases if left unchecked. The Doctor and the Algorithm combines insights from science and technology studies, critical algorithm studies, and public interest informatics to better understand the promise and peril of health AI. The book draws on case studies in automated diagnostics, algorithmic pain measurement, AI-driven drug discovery, and death prediction to investigate how health AI is made, promoted, and justified. It explores the enthusiastic promises of health AI marketing communication and medical futurism while also analyzing the inequitable outcomes new AI technology often creates for already marginalized communities. Finally, the book closes with specific recommendations for regulatory frameworks that might support more ethical and equitable approaches to health AI in the future. Interweaving textual analysis and original informatics, The Doctor and the Algorithm offers a sobering analysis of the promise of medical AI against the real and unintended consequences that deep medicine can bring for patients, providers, and public health alike.
In this second edition of Steve Fuller's original work Philosophy, Rhetoric, and the End of Knowledge: A New Beginning for Science and Technology Studies, James Collier joins Fuller in developing an updated and accessible version of Fuller's classic volume. The new edition shifts focus slightly to balance the discussions of theory and practice, and the writing style is oriented to advanced students. It addresses the contemporary problems of knowledge to develop the basis for a more publicly accountable science. The resources of social epistemology are deployed to provide a positive agenda of research, teaching, and political action designed to bring out the best in both the ancient discipline of rhetoric and the emerging field of science and technology studies (STS). The authors reclaim and integrate STS and rhetoric to explore the problems of knowledge as a social process--problems of increasing public interest that extend beyond traditional disciplinary resources. In so doing, the differences among disciplines must be questioned (the exercise of STS) and the disciplinary boundaries must be renegotiated (the exercise of rhetoric). This book innovatively integrates a sophisticated theoretical approach to the social processes of creating knowledge with a developing pedagogical apparatus. The thought questions at the end of each chapter, the postscript, and the appendix allow the reader to actively engage the text in order to discuss and apply its theoretical insights. Creating new standards for interdisciplinary scholarship and communication, the authors bring numerous disciplines into conversation in formulating a new kind of rhetoric geared toward greater democratic participation in the knowledge-making process. This volume is intended for students and scholars in rhetoric of science, science studies, philosophy, and communication, and will be of interest in English, sociology, and knowledge management arenas as well.
This book proposes three liability regimes to combat the wide responsibility gaps caused by AI systems – vicarious liability for autonomous software agents (actants); enterprise liability for inseparable human-AI interactions (hybrids); and collective fund liability for interconnected AI systems (crowds). Based on information technology studies, the book first develops a threefold typology that distinguishes individual, hybrid and collective machine behaviour. A subsequent social science analysis specifies the socio-digital institutions related to this threefold typology. Then it determines the social risks that emerge when algorithms operate within these institutions. Actants raise the risk of digital autonomy, hybrids the risk of double contingency in human-algorithm encounters, crowds the risk of opaque interconnections. The book demonstrates that the law needs to respond to these specific risks, by recognising personified algorithms as vicarious agents, human-machine associations as collective enterprises, and interconnected systems as risk pools – and by developing corresponding liability rules. The book relies on a unique combination of information technology studies, sociological institution and risk analysis, and comparative law. This approach uncovers recursive relations between types of machine behaviour, emergent socio-digital institutions, their concomitant risks, legal conditions of liability rules, and ascription of legal status to the algorithms involved.