戻る Agenda
The Relationship Between Data, AI, and Bias
Session Chair(s)
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.
Speaker(s)
Incorporating Context and Causation in Observational Real World Data: From Propensity Scores to Targeted Learning
Andrew Wilson, PhD, MS
Parexel, United States
Head of Innovative RWD Analytics
Algorithmic Bias in Healthcare Triage
Eric Siegel, PhD, MS
Predictive Analytics World, United States
Founder
Representation of Diverse Groups in Test Sets
Terri L. Cornelison, MD, PhD
FDA, United States
Chief Medical Officer and Director for the Health of Women, CDRH