Episode 31 of The Data Science Podcast with Fexingo examines a growing crisis in applied AI: models that perform perfectly in testing but cause real-world harm. Lucas and Luna dissect a 2025 case where a major hospital network's sepsis prediction algorithm silently amplified racial disparities for two years before a junior data scientist flagged it. They walk through what went wrong — biased training labels, no fairness testing in deployment, and a missing ethics review process. Then they explore the solution: internal ethics review boards that audit models for bias, transparency, and accountability before launch. With concrete examples from healthcare and hiring, this episode gives listeners a practical framework to prevent their own models from becoming the next headline. No jargon, no hype — just the hard questions every data team should be asking.

#DataScience #MachineLearning #Ethics #AI #Bias #AlgorithmicFairness #HealthcareAI #SepsisPrediction #ModelAudit #ResponsibleAI #DataEthics #MLOps #TechPolicy #FexingoBusiness #BusinessPodcast #Technology #DataGovernance #FairnessTesting

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