ABSTRACT
Chapter 8 turns to identify the ethical issues in accuracy including choosing the outcome variable, measuring accuracy, and the problem of creating accuracy. (1) The creation of the outcome variable chosen has implications as to what the organization thinks is important and whose interests are prioritized in the design of the data analytics program. (2) Accuracy is often used as the measure of a working data analytics program, where accuracy is how well the program predicts or categorizes people. Accuracy for the majority (even if meaningful) does not mean a program is accurate for all groups. (3) We face the challenge in prediction data analytics in creating accuracy, where individuals with a particular outcome variable are treated differently than those with a different predictive score—making measuring accuracy very messy. Predictive analytics runs into the possible problem of creating accuracy in categorizing someone with an outcome variable (promotable, hirable, trustworthy, high likelihood of recidivism, etc.) which pushes the individual into a course of treatment that then creates the outcome predicted by the program. The readings include Rachel Thomas and David Uminsky on issues with measuring outcomes and Kirsten Martin on designing algorithms to account for the inevitable mistakes. The related case is a predictive analytics program labeling students as future criminals.
