Direct and Indirect Discrimination Prevention Methods - Discrimination and Privacy in the Information Society - page 231

Chapter 13
Direct and Indirect Discrimination Prevention
Methods
Sara Hajian and Josep Domingo-Ferrer *
Abstract. Along with privacy, discrimination is a very important issue when consi-
dering the legal and ethical aspects of data mining. It is more than obvious that most
people do not want to be discriminated because of their gender, religion, nationality,
age and so on, especially when those attributes are used for making decisions about
them like giving them a job, loan, insurance, etc. Discovering such potential biases
and eliminating them from the training data without harming their decision-making
utility is therefore highly desirable. For this reason, anti-discrimination techniques
including discrimination discovery and prevention have been introduced in data
mining. Discrimination prevention consists of inducing patterns that do not lead to
discriminatory decisions even if the original training datasets are inherently biased.
In this chapter, by focusing on the discrimination prevention, we present a taxonomy
for classifying and examining discrimination prevention methods. Then, we intro-
duce a group of pre-processing discrimination prevention methods and specify the
different features of each approach and how these approaches deal with direct or in-
direct discrimination. A presentation of metrics used to evaluate the performance of
those approaches is also given. Finally, we conclude our study by enumerating inter-
esting future directions in this research body.
13.1 Introduction
Unfairly treating people on the basis of their belonging to a specific group,
namely race, ideology, gender, etc., is known as discrimination. In law, economics
and social sciences, discrimination has been studied over the last decades and
anti-discrimination laws have been adopted by many democratic governments.
Some examples are the US Employment Non-Discrimination Act (United States