Flexible group fairness metrics for survival analysis
Övrigt konferensbidrag, 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.
Författare
Raphael Sonabend
Technische Universität Kaiserslautern
Imperial College London
University of Cambridge
Florian Pfisterer
Ludwig-Maximilians-Universität München (LMU)
Alan Mishler
J.P. Morgan Chase
Moritz Schauer
Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik
Göteborgs universitet
Lukas Burk
Ludwig-Maximilians-Universität München (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,
Ämneskategorier (SSIF 2025)
Sannolikhetsteori och statistik
Datavetenskap (datalogi)
Nationalekonomi