Ethical Reasoning in Big Data: An Exploratory Analysis

ISBN-13: 9783319284200 / Angielski / Twarda / 2016 / 192 str.

Sorin Adam Matei; Jeff Collmann

Opis

This volume gathers seminal contributions that identify the boundaries of the various ethical dilemmas that have emerged in the field of big data research and proposes a set of guidelines for solving or preventing these dilemmas. The book discusses data collection and handling, theoretical and practical definitions of privacy, human and social costs of big data collection and analysis, the economic impact of ethical costs, political dimensions of big data ethics, ethical and unethical behavior in 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 the factors which may impact fundamental definitions of ethical use and abuse of big data use. 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 results, processes, or tools related to big data projects. The guidelines and principles can shape future research, the design of new practices, tool development, commercial, political, or administrative use of big data.The book aims to reach 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, raw or in the form of analysis results. Finally, practitioners can use it to steer future tools or procedures for handling big data. As this is an area of great interest, yet largely undeveloped, we anticipate that the volume will attract significant interest filling in an obvious gap."

Recenzje

"Ethical reasoning in big data: an exploratory analysis is a timely book on the ethical aspects of big data. ... Big data analytics is a very important tool for future society. Used properly, it can benefit generations to come. Keep in mind that governments and corporations could also use it to invade people's privacy and well being. Therefore, everyone should keep an eye on big data development." (R. S. Chang, Computing Reviews, March, 2017)

"This book tries to analyze the meaning of privacy ... . It looks at specific domains, including the American public's attitudes; ways to build trust while analyzing large bodies of data; genomics research; and personal privacy on the internet. ... Summing Up: Recommended. All readers." (H. Levkowitz, Choice, Vol. 54 (6), February, 2017)

"Nearly 20 US-affiliated researchers contributed ten chapters to this collection, which originated at 'Privacy in the Infosphere: an NSF-Sponsored Workshop on Ethical Analysis of Big Data,' at Georgetown University. ... a variety of practitioners, researchers, and students will find this material of interest. ... big data is here and we must think about the ethical implications of how data is collected, stored, and utilized. This is the strength of this work." (Brad Reid, Computing Reviews, computingreviews.com, October, 2016)

Spis treści

Part  I. Applying a contextual analysis of privacy in Big Data research._A theoretical framework for ethical reflection in big data research.- Part II. Ethical reasoning beyond privacy in Big Data.- The Privacy Preferences of Americans.- Beyond TIA: government surveillance in the post-Snowden era.- Engaging the public in ethical reasoning about Big Data.- The Ethics of Large-Scale Genomic Research.- Neurotechnological Convergence and 'Big Data:A Force-Multiplier toward Advancing Neuroscience.- Assuring personal privacy on the Internet.- Part III. Institutionalizing ethical reasoning about Big Data.- Technology for Privacy Assurance.- Institutionalizing ethical reasoning: Integrating the ASA’s Ethical Guidelines for Professional Practice into course, program, and curriculum.- Data Management Plans, Institutional Review Boards, and the Ethical Management of Big Data about Human Subjects.- Integrating ethical reasoning into preparation for participation to work in and with Big Data through the Stewardship model.   

O autorze

Sorin Adam Matei
Purdue University
100 N. University Drive
West Lafayette, IN 46077

Jeff Collmann
Office of the Senior Vice President for Research
Georgetown University
37th and O Streets, N.W.
Washington, DC 20015

Blurb

This book springs from a multidisciplinary, multi-organizational, and multi-sector conversation about the privacy and ethical implications of research in human affairs using big data. The need to cultivate and enlist the public’s trust in the abilities of particular scientists and scientific institutions constitutes one of this book’s major themes. The advent of the Internet, the mass digitization of research information, and social media brought about, among many other things, the ability to harvest – sometimes implicitly – a wealth of human genomic, biological, behavioral, economic, political, and social data for the purposes of scientific research as well as commerce, government affairs, and social interaction. What type of ethical dilemmas did such changes generate? How should scientists collect, manipulate, and disseminate this information? The effects of this revolution and its ethical implications are wide-ranging. This book includes the opinions of myriad investigators, practitioners, and stakeholders in big data on human beings who also routinely reflect on the privacy and ethical issues of this phenomenon. Dedicated to the practice of ethical reasoning and reflection in action, the book offers a range of observations, lessons learned, reasoning tools, and suggestions for institutional practice to promote responsible big data research on human affairs. It caters to 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. Lastly, 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.