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October 31, 2025Journal of Artificial Intelligence ResearchOpen Access

Causal Graphs and Fairness in Machine Learning: Addressing Practical Challenges in Causal Fairness Evaluation

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Authors

LCLea CohauszTechnische Hochschule MannheimJKJakob KappenbergerTechnische Hochschule MannheimHSHeiner StuckenschmidtTechnische Hochschule Mannheim

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Overview

This research demonstrates fairness measures in machine learning through causal structures, indicating improved bias mitigation strategies.

Key Points

  • Fairness measures are enhanced through clear causal structures and understanding context, promoting better bias mitigation.
  • Incorporating background knowledge improves the accuracy of graphical models, leading to more effective fairness evaluations.
  • Observational analysis of causal structures shows that different graphical arrangements impact fairness outcomes significantly, with real-world implications.
  • The research highlights the need for structure learning algorithms to construct more reliable graphs for fairness assessment.

Cite This Study

Cohausz et al. (2025) studied this question.

synapsesocial.com/papers/6903feedb25c631a426600dahttps://doi.org/10.1613/jair.1.19082
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  5. 5Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning2024 · 18 citations