Duran, Juan Manuel (2023) Machine learning, justification, and computational reliabilism. [Preprint]

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Abstract

This article asks the question, ``what is reliable machine learning?'' As I intend to answer it, this is a question about epistemic justification. Reliable machine learning gives justification for believing its output. Current approaches to reliability (e.g., transparency) involve showing the inner workings of an algorithm (functions, variables, etc.) and how they render outputs. We then have justification for believing the output because we know how it was computed. Thus, justification is contingent on what can be shown about the algorithm, its properties, and its behavior. In this paper, I defend computational reliabilism (CR). CR is a computationally-inspired off-shoot of process reliabilism that does not require showing the inner workings of an algorithm. CR credits reliability to machine learning by identifying reliability indicators external to the algorithm (validation methods, knowledge-based integration, etc.). Thus, we have justification for believing the output of machine learning when we have identified the appropriate reliability indicators. CR is advanced as a more suitable epistemology for machine learning. The main goal of this article is to lay the groundwork for CR, how it works, and what we can expect as a justificatory framework for reliable machine learning.

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Item Type:	Preprint
Creators:	Creators Email ORCID Duran, Juan Manuel j.m.duran@tudelft.nl 0000-0001-6482-0399
Keywords:	Computational reliabilism Justification Machine learning
Subjects:	Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence General Issues > History of Philosophy of Science General Issues > Social Epistemology of Science
Depositing User:	Dr Juan Duran
Date Deposited:	01 Nov 2023 23:44
Last Modified:	01 Nov 2023 23:44
Item ID:	22726
Subjects:	Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence General Issues > History of Philosophy of Science General Issues > Social Epistemology of Science
Date:	2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22726

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