International Day Of Women in Statistics and Data Science: Full Schedule

Registration opens August 15th at www.idwsds.org.

You will be able to validate your attendance (Check-In to sessions) using your personal schedule.

07:30 UTC

S108 - Explainable AI in Health Technology

Tuesday October 6, 2026 07:30 - 08:00 UTC

The rapid deployment of machine learning in healthcare has exposed a fundamental tension between model performance and clinical utility. This talk addresses the urgent need for Explainability (XAI), moving beyond the "black box" paradigm to ensure that AI systems are not only accurate but also transparent, accountable, and trustworthy. In the high-stakes environment of medical diagnostics, understanding an algorithm’s pathway is essential for verifying clinical reliability and meeting the rigorous standards demanded by both clinicians and regulators.
A central theme of this talk is the transition from surface-level performance metrics to deep reproducibility and validation. We will examine how traditional accuracy scores can sometimes be deceptive and how to combat this, compare XAI with true interpretability, and tackle the challenge of the inheritance of bias. This session outlines proactive mitigation strategies, including rigorous data quality checking, subgroup analysis, and continuous fairness assessments throughout the model lifecycle.
Finally, the talk will navigate the evolving regulatory landscape, specifically the EU AI Act.

Speakers

Autumn Johnson

University of Galway

My name is Autumn Johnson, and I’m a postdoctoral researcher in Statistics at the University of Galway. My postdoctoral work has focused on health technology advancements using complex statistical methods, machine learning, and AI. My PhD is from University College Cork, where I... Read More →

Organizers

Autumn Johnson

University of Galway

Tuesday October 6, 2026 07:30 - 08:00 UTC
Zoom Room #1

Plenary Talk, Technical Statistical Session

Country Ireland
Session ID 108

International Day Of Women in Statistics and Data Science

07:30 UTC

Autumn Johnson

Autumn Johnson

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