Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review
This work addresses algorithmic bias in public health ML to prevent reinforcement of health disparities, though it is incremental as it builds on existing frameworks.
The authors conducted a systematic review of algorithmic bias in Dutch public health machine learning research from 2021-2025 using a new assessment tool (RABAT), finding pervasive gaps in fairness framing and transparency across 35 studies. They introduced a four-stage fairness framework (ACAR) with recommendations to help practitioners address bias and promote health equity.
Machine learning (ML) promises to revolutionize public health through improved surveillance, risk stratification, and resource allocation. However, without systematic attention to algorithmic bias, ML may inadvertently reinforce existing health disparities. We present a systematic literature review of algorithmic bias identification, discussion, and reporting in Dutch public health ML research from 2021 to 2025. To this end, we developed the Risk of Algorithmic Bias Assessment Tool (RABAT) by integrating elements from established frameworks (Cochrane Risk of Bias, PROBAST, Microsoft Responsible AI checklist) and applied it to 35 peer-reviewed studies. Our analysis reveals pervasive gaps: although data sampling and missing data practices are well documented, most studies omit explicit fairness framing, subgroup analyses, and transparent discussion of potential harms. In response, we introduce a four-stage fairness-oriented framework called ACAR (Awareness, Conceptualization, Application, Reporting), with guiding questions derived from our systematic literature review to help researchers address fairness across the ML lifecycle. We conclude with actionable recommendations for public health ML practitioners to consistently consider algorithmic bias and foster transparency, ensuring that algorithmic innovations advance health equity rather than undermine it.