Predicting bias in machine learned classifiers using clustering

Abstract

We investigate the problem of diagnosing bias in machine learned classifiers by examining performance on the clusters of a testing set. We propose an algorithm for predicting and mitigating the relative bias in the classifier. We examine the performance of this algorithm in a few well-studied datasets and discuss potential applications.