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DIA 2021 Global Annual Meeting

The Relationship Between Data, AI, and Bias

Session Chair(s)

David O Olaleye, PhD, MSc

David O Olaleye, PhD, MSc

  • Senior Manager and Principal Research Statistician
  • SAS Institute Inc., United States
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 Objective : 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.


Andrew  Wilson, PhD, MS

Incorporating Context and Causation in Observational Real World Data: From Propensity Scores to Targeted Learning

Andrew Wilson, PhD, MS

  • Head of Innovative RWD Analytics
  • Parexel, United States
Eric  Siegel, PhD, MS

Algorithmic Bias in Healthcare Triage

Eric Siegel, PhD, MS

  • Founder
  • Predictive Analytics World, United States
Terri L. Cornelison, MD, PhD

Representation of Diverse Groups in Test Sets

Terri L. Cornelison, MD, PhD

  • Chief Medical Officer and Director for the Health of Women, CDRH
  • FDA, United States