Flexible Group Fairness Metrics for Survival Analysis

@article{Sonabend2022FlexibleGF,
  title={Flexible Group Fairness Metrics for Survival Analysis},
  author={Raphael Sonabend and Florian Pfisterer and Alan Mishler and Moritz Schauer and Lukas Burk and Sandra Jeanne Vollmer},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.03256}
}
Algorithmic fairness is an increasingly important field concerned with de- tecting and mitigating biases in machine learning models. There has been a wealth of literature for algorithmic fairness in regression and classification however there has been little exploration of the field for survival analysis. Survival analysis is the prediction task in which one attempts to predict the probability of an event occurring over time. Survival predictions are particularly important in sensitive settings… 

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