Author: Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton
Publisher: Modern Data Science with R, 2nd edition
Publication Year: 2021
Summary: The following textbook chapter is about data ethics, but more specifically, a technical data science guide that puts so much emphasis on data ethics. The chapter begins by showing some clear examples of data visualizations being used to “lie with numbers,” such as inverting the y-axis to make an unfocused reader make the opposite conclusion. It then talks about systematic racism’s history in the United States and shows how data could be used to reinforce stereotypes. The textbook seems to focus on the principles of the Data Science Oath, where it is important that these numbers are related to real people. The chapter itself focuses on all categories of data science that could be done unethically, including reproducibility of results, and does not mention many guidelines itself. It does, however, link to several sources for data ethics guidelines, such as the American Statistical Association framework.