Fairness in Machine Learning meets with Equity in Healthcare

TL;DR

This paper introduces an AI framework based on software engineering principles to identify and reduce biases in healthcare machine learning models, aiming to promote fairness and health equity.

Contribution

It presents a novel AI framework for bias mitigation in healthcare ML models, grounded in software engineering, with a case study demonstrating its effectiveness.

Findings

Biases in data can amplify in model predictions

Machine learning methods can help prevent bias propagation

Framework shows potential to improve fairness in healthcare

Abstract

With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes. However, this also brings the risk of perpetuating biases in data and model design that can harm certain demographic groups based on factors such as age, gender, and race. This study proposes an artificial intelligence framework, grounded in software engineering principles, for identifying and mitigating biases in data and models while ensuring fairness in healthcare settings. A case study is presented to demonstrate how systematic biases in data can lead to amplified biases in model predictions, and machine learning methods are suggested to prevent such biases. Future research aims to test and validate the proposed ML framework in real-world clinical settings to evaluate its impact on promoting health equity.