Abstract:Fairness in machine learning has attained significant focus due to the widespread application of machine learning in high-stake decision-making tasks. Unless regulated with a fairness objective, machine learning classifiers might demonstrate unfairness/bias towards certain demographic populations in the data. Thus, the quantification and mitigation of the bias induced by classifiers have become a central concern. In this paper, we aim to quantify the influence of different features on the bias of a classifier.… Show more
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