I am a senior research scientist at Google Research. My work focuses on the incorporation of fairness, distribution shift, and equity considerations into the design and evaluation of machine learning systems in healthcare contexts. Previously, I completed a PhD in Biomedical Informatics at Stanford University in the Department of Biomedical Data Science.

For a complete list of publications, see my Google Scholar page.

Select Publications

A toolbox for surfacing health equity harms and biases in large language models
Stephen R. Pfohl*, Heather Cole-Lewis*, Rory Sayres, Darlene Neal, Mercy Asiedu, Awa Dieng, Nenad Tomasev, Qazi Mamunur Rashid, Shekoofeh Azizi, Negar Rostamzadeh, Liam G. McCoy, Leo Anthony Celi, Yun Liu, Mike Schaekermann, Alanna Walton, Alicia Parrish, Chirag Nagpal, Preeti Singh, Akeiylah Dewitt, Philip Mansfield, Sushant Prakash, Katherine Heller, Alan Karthikesalingam, Christopher Semturs, Joelle Barral, Greg Corrado, Yossi Matias, Jamila Smith-Loud, Ivor Horn, Karan Singhal
Nature Medicine (2024)
[paper] [pdf]

A Causal Perspective on Label Bias
Vishwali Mhasawade, Alexander D’Amour, Stephen R. Pfohl
FAccT ‘24: The 2024 ACM Conference on Fairness, Accountability, and Transparency
[paper] [pdf]

Proxy Methods for Domain Adaptation
Katherine Tsai, Stephen R. Pfohl, Olawale Salaudeen, Nicole Chiou, Matt Kusner, Alexander D’Amour, Sanmi Koyejo, Arthur Gretton
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:3961-3969, 2024.
[paper] [pdf]

Understanding subgroup performance differences of fair predictors using causal models
Stephen R. Pfohl, Natalie Harris, Chirag Nagpal, David Madras, Vishwali Mhasawade, Olawale Salaudeen, Katherine Heller, Sanmi Koyejo, Alexander D’Amour
NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation Models
[paper] [pdf]

Adapting to Latent Subgroup Shifts via Concepts and Proxies
Ibrahim Alabdulmohsin, Nicole Chiou, Alexander D’Amour, Arthur Gretton, Sanmi Koyejo, Matt J. Kusner, Stephen R. Pfohl, Olawale Salaudeen, Jessica Schrouff, Katherine Tsai
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:9637-9661, 2023
Equal contribution; authors listed alphabetically.
[paper] [pdf]

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
ACM Conference on Fairness Accountability and Transparency (FAccT) 2022
[preprint] [paper] [pdf]

Recommendations for algorithmic fairness assessments of predictive models in healthcare: evidence from large-scale empirical analyses
Stephen R. Pfohl
[dissertation] [pdf] [slides]

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
[preprint] [paper]

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] [preprint] [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]