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Thursday, July 1 • 2:00pm - 3:00pm
#438 SL: The Relationship Between Data, AI, and Bias

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Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-21-660-L04-P; CME 1.00; IACET 1.00; RN 1.00

Predictive models generated by machine learning can exhibit inequitable behavior, with differing false positive rates between protected groups. We will describe how this type of bias and inequity can arise in models used for healthcare triage. In the second talk, we evaluate and assess how targeted learning and other state of the art and tools available for data-adaptive learning can be used to estimate and compare causal inference models. We will explore real-world observational data challenges, including missing data, confounding, and other issues. Using causal tools, from propensity scores to targeted learning, we will investigate this rich frontier of statistical science.

Learning Objectives

Describe machine learning algorithmic bias and how to evaluate predictive models for equity; Identify the state of the art and tools for data-adaptive learning and applications to causal inference and machine learning models.


David O Olaleye, PhD, MSc


Incorporating Context and Causation in Observational Real World Data: From Propensity Scores to Targeted Learning
Andrew Wilson, PhD, MS

Algorithmic Bias in Healthcare Triage
Eric Siegel, PhD, MS

Representation of Diverse Groups in Test Sets
Terri L. Cornelison, MD, PhD

avatar for Eric Siegel

Eric Siegel

Founder, Predictive Analytics World, United States
Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is the founder of the long-running Predictive Analytics World and the Deep Learning World conference series, which have served more than... Read More →
avatar for Andrew Wilson

Andrew Wilson

Scientific Lead, Scientific Data Organization, Parexel, United States
I am the Scientific Lead within the Scientific Data Organization at Parexel. My interests are in real-world data applications to scientific questions and the importance of embracing context within the 'data generation process.' Ongoing research is along the intersection of machine... Read More →
avatar for David Olaleye

David Olaleye

Senior Manager and Principal Research Statistician, SAS Institute Inc., United States
David Olaleye is a Senior Manager/Principal Research Statistician at SAS Institute, Cary, NC. He received his postgraduate training in demography and statistics, and clinical epidemiology from the University of Pennsylvania School of Arts and Sciences, and School of Medicine, Philadelphia... Read More →
avatar for Terri Cornelison

Terri Cornelison

Chief Medical Officer and Director for the Health of Women, CDRH, FDA, United States
Appointed first Chief Medical Officer for the Health of Women. Directing and expanding modernized Health of Women Program with an overarching mission to improve the health of all women. Key focus is to improve availability, analysis, and communication of sex- and gender-specific information... Read More →

Thursday July 1, 2021 2:00pm - 3:00pm EDT
TBD Virtual Event Horsham, PA 19044
  03: Data-Data Standards, Session |   11: Statistics, Session