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Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Jessica Schrouff · Natalie Harris · Sanmi Koyejo · Ibrahim Alabdulmohsin · Eva Schnider · Krista Opsahl-Ong · Alexander Brown · Subhrajit Roy · Diana Mincu · Christina Chen · Awa Dieng · Yuan Liu · Vivek Natarajan · Alan Karthikesalingam · Katherine Heller · Silvia Chiappa · Alexander D'Amour

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #118

Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings. Importantly, the success of any mitigation strategy strongly depends on the \textit{structure} of the shift. Despite this, there has been little discussion of how to empirically assess the structure of a distribution shift that one is encountering in practice. In this work, we adopt a causal framing to motivate conditional independence tests as a key tool for characterizing distribution shifts. Using our approach in two medical applications, we show that this knowledge can help diagnose failures of fairness transfer, including cases where real-world shifts are more complex than is often assumed in the literature. Based on these results, we discuss potential remedies at each step of the machine learning pipeline.

Author Information

Jessica Schrouff (DeepMind)
Jessica Schrouff

I am a Senior Research Scientist at DeepMind since 2022. I joined Alphabet in 2019 as part of Google Research working on trustworthy machine learning for healthcare. Before that, I was a postdoctoral researcher at University College London and Stanford University studying machine learning for neuroscience. My current interests lie at the intersection of trustworthy machine learning and causality.

Natalie Harris (Google)
Sanmi Koyejo (Stanford, Google Research)
Sanmi Koyejo

Sanmi Koyejo is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign and a research scientist at Google AI in Accra. Koyejo's research interests are in developing the principles and practice of adaptive and robust machine learning. Additionally, Koyejo focuses on applications to biomedical imaging and neuroscience. Koyejo co-founded the Black in AI organization and currently serves on its board.

Ibrahim Alabdulmohsin (Google)
Eva Schnider (University of Basel)
Krista Opsahl-Ong
Alexander Brown (Google)
Subhrajit Roy (Google)
Diana Mincu (Google)
Christina Chen (Google)
Awa Dieng (Google Brain)

My research interests span machine learning, causal inference, fairness, and interpretability.

Yuan Liu (google inc)
Vivek Natarajan (Google Brain)

Researcher working at the intersection of AI and healthcare at Google. Research interests include improving data efficiency, robustness, generalization, safety, fairness and privacy of AI systems.

Alan Karthikesalingam (Google)
Katherine Heller (Google)
Silvia Chiappa (DeepMind)
Alexander D'Amour (Google Brain)

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