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Health equity has become an industry-wide priority, with 98% of healthcare organizations reporting that they have a health equity strategy in place, according to EY. Data and analytics play a pivotal role. Organizations are turning to machine learning (ML) algorithms or rules-based systems to allocate healthcare resources to their members. The catch? Algorithms that aren’t adequately evaluated for bias can actually make health disparities worse.

On Thursday, February 16, AIMed will sit down with ClosedLoop to discuss. ClosedLoop will explain why it’s important to assess bias prior to deployment. We'll also share their new platform features that help data science teams evaluate algorithmic bias while training and validating ML models. Additionally, you'll hear about what a customer discovered when evaluating their own predictive models for bias, and what they learned in the process.

Reserve your spot today.