Reliability in Machine Learning

Thomas Grote; Konstantin Genin; Emily Sullivan

doi:10.1111/phc3.12974

Reliability in Machine Learning

Thomas Grote^*, Konstantin Genin, Emily Sullivan

^*Corresponding author for this work

OFR - Theoretical Philosophy

University of Tübingen

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Article number	e12974
Number of pages	11
Journal	Philosophy Compass
Volume	19
Issue number	5
DOIs	https://doi.org/10.1111/phc3.12974
Publication status	Published - May 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Philosophy Compass published by John Wiley & Sons Ltd.

Funding

Deutsche Forschungsgemeinschaft, Grant/Award Number:BE5601/4-1; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Number: VI.Veni.201F.051; Carl-Zeiss-Stiftung

Funders	Funder number
Carl-Zeiss-Stiftung
Certification and Foundations of Safe Machine Learning Systems
Deutsche Forschungsgemeinschaft	390727645, EXC 2064, BE5601/4-1
Deutsche Forschungsgemeinschaft
Nederlandse Organisatie voor Wetenschappelijk Onderzoek	VI.Veni.201F.051
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Dutch Ministry of Education, Culture, and Science	024.004.031

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10.1111/phc3.12974Licence: CC BY

Philosophy Compass - 2024 - Grote - Reliability in Machine LearningFinal published version, 256 KBLicence: CC BY

Cite this

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Reliability in Machine Learning

Abstract

Bibliographical note

Funding

Access to Document

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