2021-03-18-chikahara21a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint	Machine learning is used to make decisions for individuals in various fields, which require us to achieve good prediction accuracy while ensuring fairness with respect to sensitive features (e.g., race and gender). This problem, however, remains difficult in complex real-world scenarios. To quantify unfairness under such situations, existing methods utilize path-specific causal effects. However, none of them can ensure fairness for each individual without making impractical functional assumptions about the data. In this paper, we propose a far more practical framework for learning an individually fair classifier. To avoid restrictive functional assumptions, we define the probability of individual unfairness (PIU) and solve an optimization problem where PIU’s upper bound, which can be estimated from data, is controlled to be close to zero. We elucidate why our method can guarantee fairness for each individual. Experimental results show that our method can learn an individually fair classifier at a slight cost of accuracy.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	chikahara21a	0	Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint	145	153	145-153	145	false	Chikahara, Yoichi and Sakaue, Shinsaku and Fujino, Akinori and Kashima, Hisashi	given family Yoichi Chikahara given family Shinsaku Sakaue given family Akinori Fujino given family Hisashi Kashima	2021-03-18		Proceedings of The 24th International Conference on Artificial Intelligence and Statistics	130	inproceedings	date-parts 2021 3 18	http://proceedings.mlr.press/v130/chikahara21a/chikahara21a.pdf	label link Supplementary PDF http://proceedings.mlr.press/v130/chikahara21a/chikahara21a-supp.pdf