Sullivan, Emily (2019) Understanding from Machine Learning Models. British Journal for the Philosophy of Science. ISSN 1464-3537
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Abstract
Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding.
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| Item Type: | Published Article or Volume | ||||||
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| Keywords: | understanding; explanation; how-possibly explanation; machine learning models; deep neural networks | ||||||
| Subjects: | Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Values In Science |
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| Depositing User: | Dr. Emily Sullivan | ||||||
| Date Deposited: | 02 Aug 2019 04:09 | ||||||
| Last Modified: | 02 Aug 2019 04:09 | ||||||
| Item ID: | 16276 | ||||||
| Journal or Publication Title: | British Journal for the Philosophy of Science | ||||||
| Subjects: | Specific Sciences > Computer Science General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Values In Science |
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| Date: | 2019 | ||||||
| ISSN: | 1464-3537 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/16276 |
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