Causal Fairness-Guided Dataset Reweighting using Neural Networks

Autor:	Zhao, Xuan, Broelemann, Klaus, Ruggieri, Salvatore, Kasneci, Gjergji
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: To be published in the proceedings of 2023 IEEE International Conference on Big Data (IEEE BigData 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.10512 Zobrazit plný text záznamu View this record from Arxiv