Jordy Michorius graduates on Fair Machine Learning

by Jordy Michorius.

In this research an approach for bias reduction, while still maintaining usability of the classifier, is proposed. The approach for bias reduction requires all preprocessing to be done, include one-hot encoding and making the training and test set split. The approach then requires a banned feature, a feature that has for example been deemed morally irrelevant for the classification purpose. For the bias reduction, the proposal is to use the KS-score obtained from the two sample KS-test to determine how well a feature contributes to classification and how well it contributes to the bias of the banned feature. So that means that all features present in the dataset that are not the label(L) or the banned feature(B), that the following holds for feature X to be safe to use in the training dataset:

KS–score(X|L=1, X|L=0) > KS–score(X|B=1, X|B=0)

After all features are checked, the unsafe (or flagged) features need to be removed from both the training and the test set in order to make the classifier as fair as possible. The datasets that have been used are the Titanic dataset, with as banned feature the passenger class and a Financial survey, with as banned feature the race. The results have shown that the overall bias has been reduced for both the Titanic dataset and the Financial survey. However in terms of relative fairness, the Financial survey is the only one that became less fair for a certain banned feature value (Race = White). All other values became fairer for both the Financial survey and the Titanic dataset.

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