Flexible group fairness metrics for survival analysis
Other conference contribution, 2022
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.
Author
Raphael Sonabend
Technische Universität Kaiserslautern
Imperial College London
University of Cambridge
Florian Pfisterer
Ludwig Maximilian University of Munich (LMU)
Alan Mishler
J.P. Morgan Chase
Moritz Schauer
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
University of Gothenburg
Lukas Burk
Ludwig Maximilian University of Munich (LMU)
Leibniz Institute
Sumantrak Mukherjee
Birla Institute of Technology and Science Pilani
Sebastian Vollmer
Technische Universität Kaiserslautern
DFKI
16th annual "Health Informatics meets Digital Health" DS Helath 2022
Vienna, Austria,
Vienna, Austria,
Subject Categories (SSIF 2025)
Probability Theory and Statistics
Computer Sciences
Economics