Measurement Modeling of Predictors and Outcomes in Algorithmic Fairness

MCML Authors

Christoph Kern

Prof. Dr.

Associate

Social Data Science and AI

Abstract

This contribution investigates structural equation modeling (SEM) as a pre-processing approach to mitigate measurement bias in algorithmic decision-making systems. We construct latent predictors and latent targets based on different measurement modeling strategies and evaluate their interplay in simulations and an application study. We systematically compare SEMs which preserve group-differences (group-overarching) to models which equalize group-differences (group-specific) in predictors and outcomes. In our simulations, we find that group-overarching models are a more effective strategy than group-specific models and lead to smaller subgroup prediction error and better calibrated risk scores. In the application study we apply SEM to a health risk prediction task and find support for the benefit of group-overarching models. We conclude that tackling fairness concerns by utilizing measurement models of both the predictors and the outcome can contribute to the fairness of ADM systems. Utilizing SEM during preprocessing allows to incorporate substantive knowledge about the prediction task into the model implementation.

inproceedings

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EWAF 2024

3rd European Workshop on Algorithmic Fairness. Mainz, Germany, Jul 01-03, 2024.

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Authors

E. Kraus • C. Kern

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Links

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Research Area

 C4 | Computational Social Sciences