97 Things About Ethics Everyone in Data Science Should Know

Chapter 57. Ethics and Figs: Why Data Scientists Cannot Take Shortcuts

Jennifer Lewis Priestley

Associate Dean & Dir. of the Analytics and Data Science Institute, Kennesaw State University

I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity.

Supreme Court justice Oliver Wendell Holmes Jr.

Data scientists should take a minute to reflect on the preceding quote.

Simplifying the complex is hard. Calculators, computers, and downloadable packages are all mediums of expediency of calculation rather than substitutions for computational ability.

In the rush to become a “data scientist,” many individuals are shortcutting the process—stopping at the near side of complexity. While the concept of the “citizen data scientist” has its place, too many individuals who represent themselves as data scientists have no formal training in data science beyond a weekend boot camp. The consequence is greater than just confusion related to the definition of “data scientist”—it’s a major source of the myriad ethical issues that are emerging relative to algorithmically biased outcomes.

Note that “algorithms” themselves are not biased; deep learning is no more “biased” than addition. However, both are subject to two sources of bias—human biases inherent to model specification and the data we select to build an algorithm. ...