Episode 30 dives into the documented case of a 2019 health-care algorithm used by millions of US patients that systematically assigned lower risk scores to Black patients than equally sick white patients. Lucas and Luna walk through how the model's training label — historical health-care spending — encoded structural inequality, why a team at the University of California Berkeley caught it years later, and what this means for data scientists today. They discuss the concrete fix: switching from cost-based proxies to clinical need indicators. The episode also touches on why fairness audits aren't just ethics exercises but technical debt prevention. A specific, sobering example of how bias enters models through the data that powers them, and what practitioners can do about it.

#AlgorithmicBias #HealthCareAlgorithm #DataScienceEthics #MachineLearningFairness #RacialBias #StructuredInequality #HealthcareData #RiskScoreModel #ProximalBias #FairnessAudit #BerkeleyStudy #ModelAuditing #AdFreePodcast #DataScience #Technology #FexingoBusiness #BusinessPodcast #DataDriven

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