# Assessing Fairness in Classification Parity of Machine Learning Models in Healthcare

arXiv: https://arxiv.org/abs/2102.03717 · HTML: https://mlsanity.com/paper/2102.03717/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 18 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Fairness With Minimal Harm: A Pareto-Optimal Approach For Healthcare](https://mlsanity.com/paper/1911.06935/) (2019)
- [A survey on measuring indirect discrimination in machine learning](https://mlsanity.com/paper/1511.00148/) (2015)
- [Gradient Boosted Feature Selection](https://mlsanity.com/paper/1901.04055/) (2019)

## Built on by (most cited first, 4 of 4)

- [The Impact of Differential Privacy on Group Disparity Mitigation](https://mlsanity.com/paper/2203.02745/) (2022)
- [Exploring the Unfairness of DP-SGD Across Settings](https://mlsanity.com/paper/2202.12058/) (2022)
- [Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity](https://mlsanity.com/paper/2110.10849/) (2021)
- [Fair Machine Learning in Healthcare: A Review](https://mlsanity.com/paper/2206.14397/) (2022)

## Abstract (arXiv)

Fairness in AI and machine learning systems has become a fundamental problem in the accountability of AI systems. While the need for accountability of AI models is near ubiquitous, healthcare in particular is a challenging field where accountability of such systems takes upon additional importance, as decisions in healthcare can have life altering consequences. In this paper we present preliminary results on fairness in the context of classification parity in healthcare. We also present some exploratory methods to improve fairness and choosing appropriate classification algorithms in the context of healthcare.