# (Un)fairness in Post-operative Complication Prediction Models

arXiv: https://arxiv.org/abs/2011.02036 · HTML: https://mlsanity.com/paper/2011.02036/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [On the Impact of Random Seeds on the Fairness of Clinical Classifiers](https://mlsanity.com/paper/2104.06338/) (2021)
- [Bias Discovery in Machine Learning Models for Mental Health](https://mlsanity.com/paper/2205.12093/) (2022)
- [CURA: Clinical Uncertainty Risk Alignment for Language Model-Based Risk Prediction](https://mlsanity.com/paper/2604.14651/) (2026)

## Abstract (arXiv)

With the current ongoing debate about fairness, explainability and transparency of machine learning models, their application in high-impact clinical decision-making systems must be scrutinized. We consider a real-life example of risk estimation before surgery and investigate the potential for bias or unfairness of a variety of algorithms. Our approach creates transparent documentation of potential bias so that the users can apply the model carefully. We augment a model-card like analysis using propensity scores with a decision-tree based guide for clinicians that would identify predictable shortcomings of the model. In addition to functioning as a guide for users, we propose that it can guide the algorithm development and informatics team to focus on data sources and structures that can address these shortcomings.