Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values

TL;DR

This paper introduces a decision tree method that directly handles missing data without imputation, ensuring fair predictions and outperforming existing fairness techniques on real-world datasets.

Contribution

The paper presents a novel decision tree approach using missing incorporated as attribute (MIA) that integrates fairness optimization without separate imputation steps.

Findings

Outperforms existing fairness methods on real-world datasets

Effectively handles missing data without imputation

Reduces discrimination risks associated with imputed datasets

Abstract

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 theoretically analyze different sources of discrimination risks when training with an imputed dataset. Then, we propose an integrated approach based on decision trees that does not require a separate process of imputation and learning. Instead, we train a tree with missing incorporated as attribute (MIA), which does not require explicit imputation, and we optimize a fairness-regularized objective function. We demonstrate that our approach outperforms existing fairness intervention methods applied to an imputed dataset, through several experiments on real-world datasets.