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What is Causal Fairness in Machine Learning about?

This document discusses the integration of causal graphs in evaluating fairness in machine learning (ML), highlighting the need for a classification of causal structures and the challenges in constructing accurate graphical models. The authors propose advancements in learning causal graphs by combining background knowledge with data-driven methods, aiming to improve fairness assessments in ML. Their work addresses practical limitations and suggests that incorporating context and causal structures can enhanc

Author

Qe Singh

Language

EN