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

arXiv: https://arxiv.org/abs/2408.13295 · HTML: https://mlsanity.com/paper/2408.13295/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 125 references that are in the map.
- First posted on arXiv 23 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification](https://mlsanity.com/paper/1903.04561/) (2019)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [Human heuristics for AI-generated language are flawed](https://mlsanity.com/paper/2206.07271/) (2022)
- [MEDFAIR: Benchmarking Fairness for Medical Imaging](https://mlsanity.com/paper/2210.01725/) (2022)
- [Equalized odds postprocessing under imperfect group information](https://mlsanity.com/paper/1906.03284/) (2019)
- [Ensuring Fairness Beyond the Training Data](https://mlsanity.com/paper/2007.06029/) (2020)
- [Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination](https://mlsanity.com/paper/2009.12040/) (2020)
- [Group-Aware Threshold Adaptation for Fair Classification](https://mlsanity.com/paper/2111.04271/) (2021)
- [Fairness in Machine Learning meets with Equity in Healthcare](https://mlsanity.com/paper/2305.07041/) (2023)
- [Connecting Fairness in Machine Learning with Public Health Equity](https://mlsanity.com/paper/2304.04761/) (2023)
- [FairCompass: Operationalising Fairness in Machine Learning](https://mlsanity.com/paper/2312.16726/) (2023)
- [Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis](https://mlsanity.com/paper/2303.16759/) (2023)

## Built on by (most cited first, 6 of 6)

- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods](https://mlsanity.com/paper/2505.17870/) (2025)
- [Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care](https://mlsanity.com/paper/2503.16456/) (2025)
- [Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs](https://mlsanity.com/paper/2511.01187/) (2025)
- [Machine Learning and Public Health: Identifying and Mitigating Algorithmic Bias through a Systematic Review](https://mlsanity.com/paper/2510.14669/) (2025)
- [Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States](https://mlsanity.com/paper/2606.00010/) (2026)

## Abstract (arXiv)

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.