Algorithmic Bias in Healthcare AI and How to Avoid It
In this AIMed webinar, ClosedLoop explains why assessing algorithmic bias is critical for ensuring healthcare resources are fairly allocated to members. Tune in as AIMed hosts an important conversation about avoiding hidden bias in healthcare AI.
Health equity has become an industry-wide priority, and 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.
In this webinar, AIMed and ClosedLoop sat down to discuss algorithmic bias and how organizations can advance health equity with AI. Watch to learn why it’s important to assess bias prior to deployment and gain insight into ClosedLoop’s 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.
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