Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values. (arXiv:2109.10431v2 [cs.LG] UPDATED)

We investigate the fairness concerns of training a machine learning model
using data with missing values. Even though there are a number of fairness
intervention methods in the literature, most of them require a complete
training set as input. In practice, data can have missing values, and data
missing patterns can depend on group attributes (e.g. gender or race). Simply
applying off-the-shelf fair learning algorithms to an imputed dataset may lead
to an unfair model. In this paper, we first …

arxiv decision fairness imputation missing values prediction tree values