Fair Generalized Linear Mixed Models

TL;DR

This paper introduces a new algorithm for fair machine learning that accounts for stratified sampling in social survey data, improving prediction fairness and accuracy.

Contribution

It presents a novel method for fair generalized linear mixed models that handles stratified sampling and bias in social survey data.

Findings

The algorithm effectively accounts for strata correlations.

Stratified sampling impacts fairness and accuracy of predictions.

Simulation results demonstrate improved fairness in predictions.

Abstract

When using machine learning for automated prediction, it is important to account for fairness in the prediction. Fairness in machine learning aims to ensure that biases in the data and model inaccuracies do not lead to discriminatory decisions. E.g., predictions from fair machine learning models should not discriminate against sensitive variables such as sexual orientation and ethnicity. The training data often in obtained from social surveys. In social surveys, oftentimes the data collection process is a strata sampling, e.g. due to cost restrictions. In strata samples, the assumption of independence between the observation is not fulfilled. Hence, if the machine learning models do not account for the strata correlations, the results may be biased. Especially high is the bias in cases where the strata assignment is correlated to the variable of interest. We present in this paper an algorithm that can handle both problems simultaneously, and we demonstrate the impact of stratified sampling on the quality of fair machine learning predictions in a reproducible simulation study.