Fair Mixed Effects Support Vector Machine
This addresses fairness issues in automated predictions for social data where observations are not independent, though it is incremental as it combines existing fairness and mixed effects methods.
The paper tackles the problem of bias in machine learning predictions due to clustered data and sensitive attributes by developing a fair mixed effects support vector machine algorithm, demonstrating through simulation that clustered data can degrade fairness performance.
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.