An X-Ray Exam of Your Data

Abstract

In this chapter, we will dive into the question of how you can detect seeds for algorithmic biases in your data. As must have become clear from the previous chapters, we are chasing many different foes; therefore, we need to scan our data for many different types of potential issues, just as an annual health check might include a dozen procedures to check blood, urine, and various organs. With the recommendations in this chapter, my goal is to give you "a thousand eyes and a thousand ears" in six fairly easy and efficient steps. These analyses will create a set of maps where each map attempts to shade in bright red specific areas of concern, just like how an X-ray exam would reveal broken bones, ruptured organs, and swallowed cutlery. This will enable you to review all significant irregularities and (considering your context knowledge and what you have learned in this book, especially the previous chapter) decide whether there is reason for concern, and if so, what best to do to avoid an algorithmic bias.