Big Data Is Not A Monolith

Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West

Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West

Exploring issues from big-data to robotics, this volume is the first to comprehensively examine the regulatory implications of AI technology.

This book highlights research that contributes to a better understanding of emerging challenges in information systems (IS) outsourcing. Important topics covered include: how to digitally innovate through IS outsourcing; how to govern outsourced digitalization projects; how to cope with complex multi-vendor and micro-services arrangements; how to manage data sourcing and data partnerships, including issues of cybersecurity; and how to cope with the increasing demands of internationalization and new sourcing models, such as crowdsourcing, cloud sourcing and robotic process automation. These issues are approached from the client’s perspective, vendor’s perspective, or both. Given its scope, the book will be of interest to all researchers and students in the fields of Information Systems, Management, and Organization, as well as corporate executives and professionals seeking a more profound analysis of the underlying factors and mechanisms of outsourcing.

Calling for future law reform, Burdon questions if you will have privacy in a world of ubiquitous data collection.

The subjects of Privacy and Data Protection are more relevant than ever with the European General Data Protection Regulation (GDPR) becoming enforceable in May 2018. This volume brings together papers that offer conceptual analyses, highlight issues, propose solutions, and discuss practices regarding privacy and data protection. It is one of the results of the tenth annual International Conference on Computers, Privacy and Data Protection, CPDP 2017, held in Brussels in January 2017. The book explores Directive 95/46/EU and the GDPR moving from a market framing to a 'treaty-base games frame', the GDPR requirements regarding machine learning, the need for transparency in automated decision-making systems to warrant against wrong decisions and protect privacy, the riskrevolution in EU data protection law, data security challenges of Industry 4.0, (new) types of data introduced in the GDPR, privacy design implications of conversational agents, and reasonable expectations of data protection in Intelligent Orthoses. This interdisciplinary book was written while the implications of the General Data Protection Regulation 2016/679 were beginning to become clear. It discusses open issues, and daring and prospective approaches. It will serve as an insightful resource for readers with an interest in computers, privacy and data protection.

In times of current crisis, the voices of women are needed more than ever. The accumulation of war and environmental catastrophes teaches us that exploitation of people and nature through violent appropriation and enrichment for the sake of short-term self-interest exacts its price. This book presents contributions on the currently most relevant and most urgent issues: reshaping the economy, environmental problems, technology and the re-reading of history from the non-western and western tradition. With an outlook into the problems of class, race and gender in its intersectional framing, the collection offers a unique overview of current research in these fields and contributes to the renewal and contemporary presentation of feminist thought from partly concrete perspectives with regard to factual issues.

Examining the potential benefits and risks of using artificial intelligence to advance global sustainability. Drones with night vision are tracking elephant and rhino poachers in African wildlife parks and sanctuaries; smart submersibles are saving coral from carnivorous starfish on Australia's Great Barrier Reef; recycled cell phones alert Brazilian forest rangers to the sound of illegal logging. The tools of artificial intelligence are being increasingly deployed in the battle for global sustainability. And yet, warns Peter Dauvergne, we should be cautious in declaring AI the planet's savior. In AI in the Wild, Dauvergne avoids the AI industry-powered hype and offers a critical view, exploring both the potential benefits and risks of using artificial intelligence to advance global sustainability. Dauvergne finds that corporations and states often use AI in ways that are antithetical to sustainability. The competition to profit from AI is entrenching technocratic management, revving up resource extraction, and turbocharging consumption, as consumers buy new smart devices (and discard their old, less-smart ones). Smart technology is helping farmers grow crops more efficiently, but also empowering the agrifood industry. Moreover, states are weaponizing AI to control citizens, suppress dissent, and aim cyberattacks at rival states. Is there a way to harness the power of AI for environmental and social good? Dauvergne argues for precaution and humility as guiding principles in the deployment of AI.