Causal Reasoning for Algorithmic Fairness
This work addresses the problem of algorithmic fairness for stakeholders in AI ethics, but it is incremental as it primarily reviews and synthesizes existing approaches.
The paper argues that causal reasoning is essential for developing fair algorithms in decision-making, reviewing existing fairness approaches and analyzing recent causality-based methods.
In this work, we argue for the importance of causal reasoning in creating fair algorithms for decision making. We give a review of existing approaches to fairness, describe work in causality necessary for the understanding of causal approaches, argue why causality is necessary for any approach that wishes to be fair, and give a detailed analysis of the many recent approaches to causality-based fairness.