Understand, Manage, and Prevent Algorithmic Bias: A Guide for Business Users and Data Scientists

© Tobias Baer 2019

Tobias BaerUnderstand, Manage, and Prevent Algorithmic Biashttps://doi.org/10.1007/978-1-4842-4885-0_8

8. How Data Can Introduce Biases

Tobias Baer¹ 

(1)

Kaufbeuren, Germany

 

An old adage is “garbage in, garbage out,” suggesting that if you feed garbage data into even the fanciest algorithm, you will end up with garbage as output. The same could be said about biases. In this chapter, we will review the many ways deficient data can introduce biases into an algorithm. As you will see, some of these issues can be addressed by the data scientist; other issues actually need to be addressed by the individuals who in one way or the other generate the data (e.g., an insurance underwriter processing applications or a programmer updating a ...