  Algorithmic discrimination is an important aspect when data is used for
predictive purposes. This paper analyzes the relationships between
discrimination and classification, data set partitioning, and decision models,
as well as correlation. The paper uses real world data sets to demonstrate the
existence of discrimination and the independence between the discrimination of
data sets and the discrimination of classification models.
