Data Management for Causal Algorithmic Fairness
It addresses fairness in ML systems by focusing on data management for causal approaches, which is incremental as it reviews and identifies opportunities rather than proposing new methods.
The paper argues that fairness in machine learning requires causal reasoning rather than associational definitions, and reviews how data management techniques can be applied to address causal algorithmic fairness.
Fairness is increasingly recognized as a critical component of machine learning systems. However, it is the underlying data on which these systems are trained that often reflects discrimination, suggesting a data management problem. In this paper, we first make a distinction between associational and causal definitions of fairness in the literature and argue that the concept of fairness requires causal reasoning. We then review existing works and identify future opportunities for applying data management techniques to causal algorithmic fairness.