Responsible Data Science

CHAPTER 1Responsible Data Science

Data science is an interdisciplinary field that combines elements of statistics, computer science, and information technology to generate useful insights from the increasingly large datasets that are generated in the normal course of business. Data science helps organizations capture value from their data, reducing costs and increasing profits, and also enables completely new types of endeavors, such as powerful information search and self-driving cars. Sometimes, data science projects can go awry, when the predictions made by statistical and machine learning algorithms turn to be not just wrong, but biased and unfair in ways that cause harm. History has shown that the dual good and evil nature of statistical methods is not new, but rather a characteristic that was present from nearly the moment that they were conceived. However, by adjusting and supplementing statistical and machine learning methods and concepts, we can diagnose and reduce the harm that they may otherwise cause.

In popular and technical writing, these issues are often captured by the general term “ethical data science.” We use that term here, but we also use the more general phrase “responsible data science.” Ethics can refer in some usages to narrow “rules of the road” that pertain to a particular profession, such as real estate or accounting. Our goal here is broader than that: presenting a framework for the practice of data science that is ethical, but not in a narrow sense: ...