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DIA 2021 Global Annual Meeting


Fairness and Bias Detection and Mitigation in Machine Learning Algorithms: Real-World Evidence Applications and Examples

Session Chair(s)

David O Olaleye, PhD, MSc

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

Application of Fairness and Bias Detection Metrics to a Real-World Clinical Prediction Problem

David O Olaleye, PhD, MSc

  • Senior Manager and Principal Research Statistician
  • SAS Institute Inc., United States
Patrick  Hall, MS

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

  • Principal Scientist | Visiting Professor
  • BNH.AI | George Washington University, United States
Toyin  Tofade, PharmD, MS

Disparities in Medication Access and Compliance: The Role of Machine Learning

Toyin Tofade, PharmD, MS

  • Dean and Tenured Professor, College of Pharmacy
  • Howard University, United States