Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao Klaus Broelemann Salvatore Ruggieri Gjergji Kasneci
The importance of achieving fairness in machine learning models cannot be
overstated. Recent research has pointed out that fairness should be examined
from a causal perspective, and several fairness notions based on the on Pearl's
causal framework have been proposed. In this paper, we construct a reweighting
scheme of datasets to address causal fairness. Our approach aims at mitigating
bias by considering the causal relationships among variables and incorporating
them into the reweighting process. The proposed method adopts two neural
networks, whose structures are intentionally used to reflect the structures of
a causal graph and of an interventional graph. The two neural networks can
approximate the causal model of the data, and the causal model of
interventions. Furthermore, reweighting guided by a discriminator is applied to
achieve various fairness notions. Experiments on real-world datasets show that
our method can achieve causal fairness on the data while remaining close to the
original data for downstream tasks.