In this episode, Lucas and Luna explore how interpretability tools like SHAP and LIME uncovered a hidden signal in a hospital's cancer diagnosis model. They walk through a real case where the model was accurate but biased by a spurious correlation with patient age, and how a data scientist used local explanations to catch it before deployment. The conversation covers the difference between global and local interpretability, why accuracy metrics can mask dangerous blind spots, and how one hospital saved lives by asking 'why' instead of just 'how good'. Lucas and Luna also touch on the trade-off between model complexity and explainability, and why regulators are starting to demand interpretable models in healthcare. Listeners will come away with a concrete example of why interpretability isn't just a nice-to-have but a critical safety check in high-stakes machine learning.

#InterpretableML #SHAP #LIME #HealthcareAI #CancerDiagnosis #ModelBias #DataScience #MachineLearning #Explainability #FeatureImportance #ModelValidation #AIEthics #ClinicalAI #DataDriven #Podcast #Technology #FexingoBusiness #BusinessPodcast

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