Human-Centered Data Science An Introduction
by Cecilia Aragon, shion guha, marina kogan, michael muller, gina neff / mit press / 2022
“We cannot engage in data science that doesn’t account for power. Histories and systems of race and gender must be taught to data scientists, because we know terrible wrongs can occur in the making and use of data. This book is a must-read to expose the next generation of data scientists to the consequences of their work.”
—Safiya Umoja Noble, RECIPIENT OF THE 2021 MACARTHUR “Genius” FELLOWSHIp and author of Algorithms of Oppression
Human-Centered Data Science (2022) introduces an emerging interdisciplinary field around human-computer interaction, social science, statistics, and computational techniques. The book shows how data scientists can build and design ethical algorithms that use cutting-edge computational tools from the social sciences, incorporate storytelling using visualizations, and different modes of collaboration. Written by the founders of human-centered data science, the authors demonstrate how data scientists’ choices can promote a more transparent, human-centered approach to data science workflows and address biased and inequitable outcomes that may result from the collection, analysis, and distribution of large datasets. The book will aid data scientists in building rigorous and ethical algorithms by providing practical guidelines and case studies on how data scientists and their teams can use statistical and algorithmic data science techniques while considering the social implications of their work.
“By centering both the human-centric perspective and the data scientific perspective equally, the authors craft a comprehensive approach to human-centered data science. This book is a useful handbook to developing forward-thinking data science teams who will be prepared for the next iteration of machine learning.”—Rumman Chowdhury, Director of ML Ethics, Transparency, and Accountability (META), Twitter
“This book’s unique approach recognizes that data science is a craft spun with subjective design choices. It will be invaluable to students and practitioners alike.”—Chris Wiggins, Chief Data Scientist, New York Times
About the Authors:
Cecilia Aragon is Professor in the Department of Human Centered Design and Engineering at the University of Washington.
Shion Guha is Assistant Professor in the Faculty of Information at the University of Toronto.
Marina Kogan is Assistant Professor in the School of Computing at the University of Utah.
Michael Muller is a Research staff member at IBM Research.
Gina Neff is Director of the Minderoo Centre for Technology and Democracy at the University of Cambridge and Professor of Technology and Society at the Oxford Internet Institute and the Department of Sociology at the University of Oxford.
For further information refer to MIT Press link here.
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