Flexible Group Fairness Metrics for Survival Analysis

Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Josef Vollmer

Abstract:

Algorithmic fairness is an increasingly important field concerned with detecting 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 such as when utilising machine
learning for diagnosis and prognosis of patients. In this paper we explore
how to utilise existing survival metrics to measure bias with group fairness
metrics. We explore this in an empirical experiment with 29 survival datasets
and 8 measures. We find that measures of discrimination are able to capture
bias well whereas there is less clarity with measures of calibration and scoring rules. We suggest further areas for research including prediction-based
fairness metrics for distribution predictions.