2024-04-18-byun24a.md

title	software	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
Auditing Fairness under Unobserved Confounding	https://github.com/lasilab/inequity-bounds	A fundamental problem in decision-making systems is the presence of inequity along demographic lines. However, inequity can be difficult to quantify, particularly if our notion of equity relies on hard-to-measure notions like risk (e.g., equal access to treatment for those who would die without it). Auditing such inequity requires accurate measurements of individual risk, which is difficult to estimate in the realistic setting of unobserved confounding. In the case that these unobservables “explain” an apparent disparity, we may understate or overstate inequity. In this paper, we show that one can still give informative bounds on allocation rates among high-risk individuals, even while relaxing or (surprisingly) even when eliminating the assumption that all relevant risk factors are observed. We utilize the fact that in many real-world settings (e.g., the introduction of a novel treatment) we have data from a period prior to any allocation, to derive unbiased estimates of risk. We apply our framework to a real-world setting of Paxlovid allocation to COVID-19 patients, finding that observed racial inequity cannot be explained by unobserved confounders of the same strength as important observed covariates.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	byun24a	0	Auditing Fairness under Unobserved Confounding	4339	4347	4339-4347	4339	false	Byun, Yewon and Sam, Dylan and Oberst, Michael and Lipton, Zachary and Wilder, Bryan	given family Yewon Byun given family Dylan Sam given family Michael Oberst given family Zachary Lipton given family Bryan Wilder	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/byun24a/byun24a.pdf