Fair Mixed Effects Support Vector Machine

TL;DR

This paper introduces a fair mixed effects support vector machine that addresses bias due to clustered data and sensitive attributes, improving fairness and accuracy in social data analysis.

Contribution

It proposes a novel support vector machine model incorporating mixed effects to handle cluster correlations and fairness constraints simultaneously.

Findings

Simulation shows improved fairness in clustered data scenarios

Model reduces bias related to sensitive attributes and cluster effects

Demonstrates effectiveness over traditional fair learning methods

Abstract

To ensure unbiased and ethical automated predictions, fairness must be a core principle in machine learning applications. Fairness in machine learning aims to mitigate biases present in the training data and model imperfections that could lead to discriminatory outcomes. This is achieved by preventing the model from making decisions based on sensitive characteristics like ethnicity or sexual orientation. A fundamental assumption in machine learning is the independence of observations. However, this assumption often does not hold true for data describing social phenomena, where data points are often clustered based. Hence, if the machine learning models do not account for the cluster correlations, the results may be biased. Especially high is the bias in cases where the cluster assignment is correlated to the variable of interest. We present a fair mixed effects support vector machine algorithm that can handle both problems simultaneously. With a reproducible simulation study we demonstrate the impact of clustered data on the quality of fair machine learning predictions.