2024-12-23-kulkarni24a.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
Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations	The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient populations. While prior literature has focused on quantifying biases exhibited by trained DL models, demographically targeted adversarial bias attacks on DL models and its implication in the clinical environment remains an underexplored field of research in medical imaging. In this work, we demonstrate that demographically targeted label poisoning attacks can introduce undetectable underdiagnosis bias in DL models. Our results across multiple performance metrics and demographic groups like sex, age, and their intersectional subgroups show that adversarial bias attacks demonstrate high-selectivity for bias in the targeted group by degrading group model performance without impacting overall model performance. Furthermore, our results indicate that adversarial bias attacks result in biased DL models that propagate prediction bias even when evaluated with external datasets.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	kulkarni24a	0	Hidden in Plain Sight: Undetectable Adversarial Bias Attacks on Vulnerable Patient Populations	793	821	793-821	793	false	Kulkarni, Pranav and Chan, Andrew and Navarathna, Nithya and Chan, Skylar and Yi, Paul and Parekh, Vishwa Sanjay	given family Pranav Kulkarni given family Andrew Chan given family Nithya Navarathna given family Skylar Chan given family Paul Yi given family Vishwa Sanjay Parekh	2024-12-23		Proceedings of The 7nd International Conference on Medical Imaging with Deep Learning	250	inproceedings	date-parts 2024 12 23	https://raw.githubusercontent.com/mlresearch/v250/main/assets/kulkarni24a/kulkarni24a.pdf