How Biased is Your Feature?: Computing Fairness Influence Functions with Global Sensitivity Analysis. (arXiv:2206.00667v1 [cs.LG])

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. To this end,
we propose a framework …

analysis arxiv computing fairness feature global influence