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

Authors

  • Samantha Lynne Sargent Department of Philosophy, University of Waterloo, Waterloo, Canada

DOI:

https://doi.org/10.7202/1077639ar

Keywords:

Artificial Intelligence, Machine Learning, Bias, ImpactPro, Racism, Implicit Bias

Language(s):

English

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

 

Open issue, (c) Han Han Li

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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.

Issue

Vol. 4 No. 1 (2021): Open Issue

Section

Critical commentaries

License

Copyright (c) 2021 Samantha Lynne Sargent

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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