Sterkenburg, Tom F. (2024) Values in machine learning: What follows from underdetermination? [Preprint]
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Abstract
It has been argued that inductive underdetermination entails that machine learning algorithms must be value-laden. This paper offers a more precise account of what it would mean for a "machine learning algorithm" to be "value-laden," and, building on this, argues that a general argument from underdetermination does not warrant this conclusion.
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| Item Type: | Preprint | ||||||
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| Subjects: | General Issues > Confirmation/Induction Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science |
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| Depositing User: | Mr Tom Sterkenburg | ||||||
| Date Deposited: | 20 Dec 2024 14:42 | ||||||
| Last Modified: | 20 Dec 2024 14:42 | ||||||
| Item ID: | 24439 | ||||||
| Subjects: | General Issues > Confirmation/Induction Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science |
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| Date: | 2024 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/24439 |
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