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 Emma Stanley (Imperial College London)

Title: Connecting Algorithmic Fairness and Fair Outcomes in a Sociotechnical Simulation Case Study of AI-Assisted Healthcare

Abstract

Although there is a growing interest in the fairness of AI systems for healthcare, most of the current research focuses solely on evaluating and mitigating subgroup performance disparities in biased models. In our recently published article, we present an approach for understanding fairness in healthcare AI as a sociotechnical problem, and demonstrate how simulation frameworks can be used to investigate the downstream consequences of algorithmic fairness criteria. Using breast cancer screening as a case study, we show how four common fairness constraints, ranging from unconstrained performance disparities to equalized odds, produce markedly different mortality and socioeconomic outcomes over time. We further examine how clinician reliance on AI recommendations and patients' differential access to healthcare interact with algorithmic design choices to shape long-term outcomes. The results reveal that technically "fair" AI systems can still produce inequitable outcomes when deployed in real-life healthcare environments, emphasizing the need for an interdisciplinary approach to understanding and developing responsible AI.

Paper: https://www.nature.com/articles/s41467-025-67470-5

Speaker

Emma Stanley is a Postdoctoral Research Associate in Machine Learning for Imaging at Imperial College London. Her work focuses on the intersection of bias, robustness, and causality in AI for medical imaging, but she is also interested in the responsible and ethical development and implementation of AI in healthcare more broadly. As a result, her interdisciplinary research has spanned technical investigations of algorithmic bias to analyses of sociotechnical harms and global health equity impacts of AI. Emma received her PhD in Biomedical Engineering with Medical Imaging Specialization from the University of Calgary, and her BASc in Chemical and Biological Engineering from the University of British Columbia.

Google Scholar: https://scholar.google.ca/citations?user=yj9NS6UAAAAJ

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