Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
Machine Learning
2021-02-09 v1 Computers and Society
Abstract
Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous, healthcare in particular is a challenging field where accountability of such systems takes upon additional importance, as decisions in healthcare can have life altering consequences. In this paper we present preliminary results on fairness in the context of classification parity in healthcare. We also present some exploratory methods to improve fairness and choosing appropriate classification algorithms in the context of healthcare.
Keywords
Cite
@article{arxiv.2102.03717,
title = {Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare},
author = {Ming Yuan and Vikas Kumar and Muhammad Aurangzeb Ahmad and Ankur Teredesai},
journal= {arXiv preprint arXiv:2102.03717},
year = {2021}
}