# Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review

arXiv: https://arxiv.org/abs/2510.14669 · HTML: https://mlsanity.com/paper/2510.14669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 99 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 17 of 17)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Fairness Testing: Testing Software for Discrimination](https://mlsanity.com/paper/1709.03221/) (2017)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [Healthsheet: Development of a Transparency Artifact for Health Datasets](https://mlsanity.com/paper/2202.13028/) (2022)
- [Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Models](https://mlsanity.com/paper/2310.19917/) (2023)
- [FairTest: Discovering Unwarranted Associations in Data-Driven Applications](https://mlsanity.com/paper/1510.02377/) (2015)
- [Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review](https://mlsanity.com/paper/2408.13295/) (2024)
- [Connecting Fairness in Machine Learning with Public Health Equity](https://mlsanity.com/paper/2304.04761/) (2023)
- [Auditing a Dutch Public Sector Risk Profiling Algorithm Using an Unsupervised Bias Detection Tool](https://mlsanity.com/paper/2502.01713/) (2025)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Fairness in Spiking Neural Networks: Data Bias, Spurious Features, and Hardware Effects](https://mlsanity.com/paper/2605.27407/) (2026)
- [From Awareness to Action: Understanding and Overcoming the Research-Practice Gap in Algorithmic Fairness for Public Health](https://mlsanity.com/paper/2606.11214/) (2026)

## Abstract (arXiv)

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.