连接机器学习公平性与公共卫生公平
Connecting Fairness in Machine Learning with Public Health Equity
AI总结：
本文总结了机器学习公平性文献并提出一个框架，用于在公共卫生ML流程各阶段识别和缓解数据与模型偏差，以促进公平公正的健康结果。
摘要：
机器学习（ML）已成为公共卫生领域的关键工具，具有改善人群健康、诊断、治疗选择和卫生系统效率的潜力。然而，数据和模型设计中的偏差可能导致某些受保护群体出现差异，并放大医疗保健中现有的不平等。为应对这一挑战，本研究总结了关于ML公平性的重要文献，并提出了一个用于识别和缓解数据及模型中偏差的框架。该框架为在典型ML流程的不同阶段（如数据处理、模型设计、部署和评估）中纳入公平性提供了指导。为说明数据偏差对ML模型的影响，我们展示了若干示例，证明系统性偏差如何通过模型预测被放大。这些案例研究表明该框架可用于防止这些偏差，并凸显了在公共卫生领域建立公平且公正的ML模型的必要性。本工作旨在为公共卫生中ML的使用提供信息和指导，以朝着对所有人群更加伦理和公平的结果发展。
英文摘要：
Machine learning (ML) has become a critical tool in public health, offering the potential to improve population health, diagnosis, treatment selection, and health system efficiency. However, biases in data and model design can result in disparities for certain protected groups and amplify existing inequalities in healthcare. To address this challenge, this study summarizes seminal literature on ML fairness and presents a framework for identifying and mitigating biases in the data and model. The framework provides guidance on incorporating fairness into different stages of the typical ML pipeline, such as data processing, model design, deployment, and evaluation. To illustrate the impact of biases in data on ML models, we present examples that demonstrate how systematic biases can be amplified through model predictions. These case studies suggest how the framework can be used to prevent these biases and highlight the need for fair and equitable ML models in public health. This work aims to inform and guide the use of ML in public health towards a more ethical and equitable outcome for all populations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
