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Fairness and Bias Detection and Mitigation in Machine Learning Algorithms: Real-World Evidence Applications and Examples
Session Chair(s)
David O Olaleye, PHD, MSC
Senior Manager and Principal Research Statistician, SAS Institute Inc., United States
Artificial intelligence technology is now being used to support decision-making in clinical trials and medical devices. This session will discuss fairness, bias, and how to respond to discrimination and security failures in predictive modeling systems.
Learning Objective : Describe the different types of fairness metrics and algorithms for detecting and mitigating unintended machine learning algorithmic bias; Discuss a standardized workflow that focuses on artificial intelligence/machine learning model interpretability, post-hoc explanations, and discrimination testing.
Speaker(s)
David O Olaleye, PHD, MSC
Senior Manager and Principal Research Statistician, SAS Institute Inc., United States
Application of Fairness and Bias Detection Metrics to a Real-World Clinical Prediction Problem
Patrick Hall, MS
Principal Scientist | Visiting Professor, BNH.AI | George Washington University, United States
Real-World Experiences Helping Healthcare Companies Prepare For and Respond to Liabilities that Arise from Discrimination, Privacy, and Security Failures in Predictive Modeling Systems
Toyin Tofade, PHARMD, MS
Dean and Tenured Professor, College of Pharmacy, Howard University, United States
Disparities in Medication Access and Compliance: The Role of Machine Learning
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