all AI news
Flexible Group Fairness Metrics for Survival Analysis. (arXiv:2206.03256v3 [cs.CY] UPDATED)
July 25, 2022, 1:11 a.m. | Raphael Sonabend, Florian Pfisterer, Alan Mishler, Moritz Schauer, Lukas Burk, Sumantrak Mukherjee, Sebastian Vollmer
cs.LG updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Senior ML Researcher - 3D Geometry Processing | 3D Shape Generation | 3D Mesh Data
@ Promaton | Europe
Principal Data Engineer
@ RS21 | Remote
SQL/Power BI Developer
@ ICF | Virginia Remote Office (VA99)
Senior Machine Learning Engineer (Canada Remote)
@ Fullscript | Ottawa, ON
Software Engineer - MLOps.
@ Renesas Electronics | Toyosu, Japan
Junior Data Scientist / Artificial Intelligence consultant
@ Deloitte | Luxembourg, LU