Reliability in Machine Learning

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Philosophy Compass 19 (5):e12974 (2024)
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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.

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10.1111/phc3.12974

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Author Profiles

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

Citations of this work

Trust, Explainability and AI.Sam Baron - 2025 - Philosophy and Technology 38 (4):1-23.
A Minimalist Account of the Right To Explanation.Thomas Grote & Norbert Paulo - 2025 - Philosophy and Technology 38 (2):1-22.

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References found in this work

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

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