How to Remove Bias and Make Machine Learning Models Fair & Discrimination-Free at the Example of Credit Risk Data

How to Remove Bias and Make Machine Learning Models Fair & Discrimination-Free at the Example of Credit Risk Data

There have been multiple instances when a machine learning model was found to discriminate against a particular section of society, be it rejecting female candidates during hiring, systemically disapproving loans to working women, or having a high rejection rate for darker color candidates. Recently, it was found that facial recognition algorithms that are available as open-source have lower accuracy on female faces with darker skin color than vice versa. In another instance, research by CMU showed how a Google ad showed an ad for high-income jobs to men more often than women. Using credit risk data where we wanted to predict the probability of someone defaulting on a loan, we were able to shortlist features that were discriminatory in nature.
 

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