Building Fair Predictive Models.

Algorithmic fairness is an important aspect when data is used for predictive purposes. This paper analyses the sources of discrimination, and proposes sufficient conditions for building non-discriminatory/fair predictive models in the presence of context attributes. The paper then uses real world datasets to demonstrate the existence and the extent of discrimination in applications and the independence between the discrimination of datasets and the discrimination of classification models.