I am a PhD student at Stanford Computer Science, where I’m advised by Professors Sherri Rose and Carlos Guestrin. I’m a member of the Health Policy Data Science Lab, and a Technology & Racial Equity graduate fellow at the Center for Comparative Studies in Race and Ethnicity.

My research on algorithmic fairness explores statistical properties of machine learning models used in clinical settings, and their implications for health equity.

Academic Publications

Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation
Agata Foryciarz, Stephen R. Pfohl, Birju Patel, Nigam H. Shah
BMJ Health & Care Informatics 29, e100460
[paper] [code]

Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz, Nikolaos Ignatiadis, Julian Genkins, Nigam H. Shah
arXiv:2202.01906
[preprint]

A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Stephen R. Pfohl, Haoran Zhang, Yizhe Xu, Agata Foryciarz, Marzyeh Ghassemi, Nigam H. Shah.
Scientific Reports 12 (1), 1-13, 2022
[paper] [code]

An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction
Stephen R. Pfohl, Agata Foryciarz, Nigam H. Shah.
Journal of Biomedical Informatics, 113:103621, 2021
[paper] [preprint] [code]

Non-academic Publications

If We’re Not Careful, Tech Could Hurt the Fight against COVID-19
Ria Kalluri, Lauren Gillespie, Agata Foryciarz, Wren Elhai, Sanjana Srivastava, Argyri Panezi, Lisa Einstein.
Scientific American Blog, 2020.

Black-Boxed Politics: Opacity is a Choice in AI Systems.
Agata Foryciarz, Daniel Leufer, Katarzyna Szymielewicz.
Medium and Internazionale (Italian translation)
[English] [Italian]

Sztuczna Inteligencja Non-Fiction.
Anna Obem, Katarzyna Szymielewicz. Współpraca merytoryczna: Agata Foryciarz
Fundacja Panoptykon, 2020.