AI Bias in Healthcare: Using ImpactPro as a Case Study for Healthcare Practitioners’ Duties to Engage in Anti-Bias Measures

  • Samantha Lynne Sargent Department of Philosophy, University of Waterloo, Waterloo, Canada
Keywords: Artificial Intelligence, Machine Learning, Bias, ImpactPro, Racism, Implicit Bias

Abstract

The introduction of ImpactPro to identify patients with complex health needs suggests that current bias and impacts of bias in healthcare AIs stem from historically biased practices leading to biased datasets, a lack of oversight, as well as bias in practitioners who are overseeing AIs. In order to improve these outcomes, healthcare practitioners need to engage in current best practices for anti-bias training

 

Published
2021-06-01
How to Cite
[1]
Sargent SL. AI Bias in Healthcare: Using ImpactPro as a Case Study for Healthcare Practitioners’ Duties to Engage in Anti-Bias Measures. Can. J. Bioeth. 2021;4:112-6. https://doi.org/10.7202/1077639ar.
Section
Critical commentaries