Explore a 20-minute conference talk from the FAccT 2021 virtual event that delves into the critical issue of algorithmic fairness in predicting Opioid Use Disorder through machine learning techniques. Presented by A. Kilby as part of the Research Track, this talk examines the intersection of artificial intelligence, healthcare, and ethical considerations. Gain insights into the challenges and potential solutions for developing fair and unbiased algorithms in the context of addressing the opioid crisis. Learn about the implications of machine learning models in healthcare decision-making and the importance of ensuring equitable outcomes across diverse populations. Discover how researchers are working to mitigate disparities and improve the accuracy and fairness of predictive models for Opioid Use Disorder.
Algorithmic Fairness in Predicting Opioid Use Disorder using Machine Learning
Association for Computing Machinery (ACM) via YouTube
Overview
Syllabus
Algorithmic Fairness in Predicting Opioid Use Disorder using Machine Learning
Taught by
ACM FAccT Conference
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