Buijsman, Stefan (2024) Machine Learning models as Mathematics: interpreting explainable AI in non-causal terms. [Preprint]
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Abstract
We would like to have a wide range of explanations for the behaviour of machine learning systems. However, how should we understand these explanations? Typically, attempts to clarify what an explanations for questions such as ’why am I getting this output for these inputs?’ have been approached from the philosophy of science, through an analogy with scientific (and often causal) explanations. I show that ML systems are best thought of as non-causal, specifically mathematical objects. We should therefore interpret these explanations differently, through analogy with mathematical explanations. I show that this still allows us to use much of the same theoretical apparatus, and argue that the asymmetry of many of the standard ML explanations can be accounted for in virtue of the link these systems have with concrete implementations.
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| Item Type: | Preprint | ||||||
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| Keywords: | Explainable AI; Machine learning; Interpretability; Non-causal explanation; Mathematical explanation | ||||||
| Subjects: | Specific Sciences > Mathematics > Explanation Specific Sciences > Artificial Intelligence General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Dr. Stefan Buijsman | ||||||
| Date Deposited: | 16 Mar 2024 01:47 | ||||||
| Last Modified: | 16 Mar 2024 01:47 | ||||||
| Item ID: | 23201 | ||||||
| Subjects: | Specific Sciences > Mathematics > Explanation Specific Sciences > Artificial Intelligence General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | 2024 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/23201 |
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