Title: Achieving non-discrimination in prediction,
Abstract: Discrimination-aware classification is receiving an increasing attention in
data science fields. The pre-process methods for constructing a
discrimination-free classifier first remove discrimination from the training
data, and then learn the classifier from the cleaned data. However, they lack a
theoretical guarantee for the potential discrimination when the classifier is
deployed for prediction. In this paper, we fill this gap by mathematically
bounding the probability of the discrimination in prediction being within a
given interval in terms of the training data and classifier. We adopt the
causal model for modeling the data generation mechanism, and formally defining
discrimination in population, in a dataset, and in prediction. We obtain two
important theoretical results: (1) the discrimination in prediction can still
exist even if the discrimination in the training data is completely removed;
and (2) not all pre-process methods can ensure non-discrimination in prediction
even though they can achieve non-discrimination in the modified training data.
Based on the results, we develop a two-phase framework for constructing a
discrimination-free classifier with a theoretical guarantee. The experiments
demonstrate the theoretical results and show the effectiveness of our two-phase
framework.