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.
@article{arxiv.2011.02036,
title = {(Un)fairness in Post-operative Complication Prediction Models},
author = {Sandhya Tripathi and Bradley A. Fritz and Mohamed Abdelhack and Michael S. Avidan and Yixin Chen and Christopher R. King},
journal= {arXiv preprint arXiv:2011.02036},
year = {2020}
}