Background: The rapid advancement of Machine Learning (ML) represents novel opportunities to enhance public health research, surveillance, and decision-making. However, there is a lack of comprehensive understanding of algorithmic bias, systematic errors in predicted population health outcomes, resulting from the public health application of ML. The objective of this narrative review is to explore the types of bias generated by ML and quantitative metrics to assess these biases. Methods : We performed search on PubMed, MEDLINE, IEEE (Institute of Electrical and Electronics Engineers), ACM (Association for Computing Machinery) Digital Library, Science Direct, and Springer Nature. We used keywords to identify studies describing types of bias and metrics to measure these in the domain of ML and public and population health published in English between 2008 and 2023, inclusive. Results: A total of 72 articles met the inclusion criteria. Our review identified the commonly described types of bias and quantitative metrics to assess these biases from an equity perspective. Conclusion : The review will help formalize the evaluation framework for ML on public health from an equity perspective.

本研究针对机器学习在公共卫生应用中的算法偏见和系统性错误问题进行了深入探讨，填补了当前文献中的空白。通过对72篇相关文献的分析，识别了机器学习中常见的偏见类型及其量化指标，从公平的视角审视了这些偏见。研究结果为公共卫生中机器学习的公平性评估提供了一个正式的框架，有助于改善决策过程。

Exploring Bias and Prediction Metrics to Characterise the Fairness of
  Machine Learning for Equity-Centered Public Health Decision-Making: A
  Narrative Review

探索偏见和预测指标，以表征基于公平的公共卫生决策中的机器学习的公平性：叙事综述