International Day Of Women in Statistics and Data Science

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.