Abstract

Causality and ethical ML are two mostly disjoint fields in machine learning. Recently, their intersection is attracting increasing attention as models are deployed for consequential decision-making domains. However, most existing literature only sporadically explores this field. The aim of this workshop is to bring these efforts together to open a channel of discussion and potential collaborations to fill the gaps. Specifically, the workshop focuses on investigating progress in the five fields of fairness, explainability, robustness, privacy and security and accountability in ML through a causal lense.

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