Received During the 2022-2023
Academic School Year:
You Are Not Expected to Understand This by Torie Bosch (Editor); Kelly Chudler (Illustrator); Ellen Ullman (Introduction by)Leading technologists, historians, and journalists reveal the stories behind the computer coding that touches all aspects of life--for better or worse Few of us give much thought to computer code or how it comes to be. The very word "code" makes it sound immutable or even inevitable. "You Are Not Expected to Understand This" demonstrates that, far from being preordained, computer code is the result of very human decisions, ones we all live with when we use social media, take photos, drive our cars, and engage in a host of other activities. Everything from law enforcement to space exploration relies on code written by people who, at the time, made choices and assumptions that would have long-lasting, profound implications for society. Torie Bosch brings together many of today's leading technology experts to provide new perspectives on the code that shapes our lives. Contributors discuss a host of topics, such as how university databases were programmed long ago to accept only two genders, what the person who programmed the very first pop-up ad was thinking at the time, the first computer worm, the Bitcoin white paper, and perhaps the most famous seven words in Unix history: "You are not expected to understand this." This compelling book tells the human stories behind programming, enabling those of us who don't think much about code to recognize its importance, and those who work with it every day to better understand the long-term effects of the decisions they make. With an introduction by Ellen Ullman and contributions by Mahsa Alimardani, Elena Botella, Meredith Broussard, David Cassel, Arthur Daemmrich, Charles Duan, Quinn DuPont, Claire L. Evans, Hany Farid, James Grimmelmann, Katie Hafner, Susan C. Herring, Syeda Gulshan Ferdous Jana, Lowen Liu, John MacCormick, Brian McCullough, Charlton McIlwain, Lily Hay Newman, Margaret O'Mara, Will Oremus, Nick Partridge, Benjamin Pope, Joy Lisi Rankin, Afsaneh Rigot, Ellen R. Stofan, Lee Vinsel, Josephine Wolff, and Ethan Zuckerman.
Call Number: QA76.6 .Y585 2022
Publication Date: 2022-11-15
Human-Centered Data Science by Cecilia Aragon; Shion Guha; Marina Kogan; Michael Muller; Gina NeffBest practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists' choices are involved at every stage of the data science workflow-and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.