Reliability in Machine Learning

Philosophy Compass 19 (5):e12974 (2024)
  Copy   BIBTEX

Abstract

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

Analytics

Added to PP
2024-04-18

Downloads
196 (#105,248)

6 months
196 (#15,463)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

Thomas Grote
University of Tuebingen
Konstantin Genin
University of Tübingen
Emily Sullivan
Utrecht University

Citations of this work

No citations found.

Add more citations

References found in this work

Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Reliabilist Epistemology.Alvin Goldman & Bob Beddor - 2021 - Stanford Encyclopedia of Philosophy.
Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.

View all 36 references / Add more references