Erschienen: 03.05.2016 Abbildung von Matei / Collmann | Ethical Reasoning in Big Data | 1st ed. 2016 | 2016 | An Exploratory Analysis

Matei / Collmann

Ethical Reasoning in Big Data

An Exploratory Analysis

1st ed. 2016 2016. Buch. x, 192 S. 14 Farbabbildungen, 20 Farbtabellen, Bibliographien. Hardcover

Springer. ISBN 978-3-319-28420-0

Format (B x L): 15,5 x 23,5 cm

Gewicht: 473 g

In englischer Sprache

Das Werk ist Teil der Reihe: Computational Social Sciences


This volume comprises seminal contributions that identify the boundaries of various ethical dilemmas that have emerged in the field of big data research and proposes a set of guidelines for solving or preventing such quandaries. The book discusses data collection and handling, theoretical and practical definitions of privacy, the human and social costs of big data collection and analysis, the economic impact of ethical costs, the political dimensions of big data ethics, and ethical and unethical behavior in the commercial utilization of big data. Of particular interest are new methodologies for handling and framing the use of big data repositories. Equally important are contributions that reveal factors that may impact fundamental definitions of ethical big data use and abuse. While the first section of the book focuses on the social and theoretical roots of ethical reasoning, the second section proposes guidelines or heuristic principles for activities related to the ethical collecting, processing, analyzing, and disseminating of results, processes, or tools related to big data projects. These guidelines and principles can shape future research, new practice designs, tool development, and the commercial, political, or administrative use of big data. The book is aimed at a broad audience of educators, researchers, and practitioners. Educators can use the volume in courses related to big data handling and processing. Researchers can use it for designing new methods of collecting, processing, and disseminating big data, whether in raw form or as analysis results. Finally, practitioners can use it to steer future tools or procedures for handling big data. As this topic represents an area of great interest that still remains largely undeveloped, this book is sure to attract significant interest by filling an obvious gap in currently available literature.


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