Sullivan, Emily (2022) Inductive Risk, Understanding, and Opaque Machine Learning Models. [Preprint]
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Abstract
Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this paper, I argue that non-epistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes.
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| Item Type: | Preprint | ||||||
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| Keywords: | inductive risk, model-based science, machine learning, epistemic opacity, scientific understanding | ||||||
| Subjects: | General Issues > Data Specific Sciences > Artificial Intelligence > AI and Ethics General Issues > Ethical Issues 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: | 24 Apr 2022 18:15 | ||||||
| Last Modified: | 24 Apr 2022 18:15 | ||||||
| Item ID: | 20507 | ||||||
| Subjects: | General Issues > Data Specific Sciences > Artificial Intelligence > AI and Ethics General Issues > Ethical Issues General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Values In Science |
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| Date: | April 2022 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/20507 |
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