University Book Store Presents Dr. Cecilia Aragon with Marina Kogen, Michael Muller, & Shion Guha

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About the book: Best 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.
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Dr. Cecilia Aragon is the director of the Human-Centered Data Science Lab at the University of Washington. Her research focuses on enabling humans to explore and gain insight from vast data sets. This emerging field, known as human-centered data science, is situated at the intersection of human-computer interaction and data science.