The Statistical Methods for Health Equity Series is a monthly online series co-hosted by the Data Science for Health Equity community, the Alan Turing Institute Health Equity Interest Group, and the Department of Statistical Science at University College London.

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We are excited to announce the next instalment of our webinar series with speaker Stephen Pfohl from Google Research.

Specific details on the topic are as below:

Topic: 

Understanding challenges to the validity of evaluations of algorithmic fairness in healthcare

Abstract:

In this talk, I aim to present insights into the design of evaluations of machine learning and AI systems to assess properties related to algorithmic fairness and health equity. I argue that such evaluations are contextual and require specification of the intended use, target population, and assumptions regarding the data generating process and measurement mechanism. Through examples from my research, I argue that causal graphical models can serve as key tools for context specification and can aid in understanding and appropriate use of analytical algorithmic fairness techniques.

Speaker Biography:

Stephen Pfohl is a senior research scientist at Google Research. His work focuses on the incorporation of fairness, distribution shift, and equity considerations into the design and evaluation of machine learning systems in healthcare contexts. Stephen earned his PhD in Biomedical Informatics from Stanford University.

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Please direct any questions about the webinar series to info@datascienceforhealthequity.com.