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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)
Application of Fairness and Bias Detection Metrics to a Real-World Clinical Prediction Problem
David O Olaleye, PhD, MSc
SAS Institute Inc., United States
Senior Manager and Principal Research Statistician
Real-World Experiences Helping Healthcare Companies Prepare For and Respond to Liabilities that Arise from Discrimination, Privacy, and Security Failures in Predictive Modeling Systems
Patrick Hall, MS
BNH.AI | George Washington University, United States
Principal Scientist | Visiting Professor
Disparities in Medication Access and Compliance: The Role of Machine Learning
Toyin Tofade, PharmD, MS
Howard University, United States
Dean and Tenured Professor, College of Pharmacy
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