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.
