Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare

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,...

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Published in:arXiv.org 2021-02
Main Authors: Yuan, Ming, Kumar, Vikas, Ahmad, Muhammad Aurangzeb, Teredesai, Ankur
Format: Article
Language:English
Subjects:
Accountability
Algorithms
Artificial intelligence
Classification
Context
Health care
Machine learning
Parity
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Kumar, Vikas
Ahmad, Muhammad Aurangzeb
Teredesai, Ankur
description 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.
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subjects Accountability
Algorithms
Artificial intelligence
Classification
Context
Health care
Machine learning
Parity
title Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare
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