Ratti, Emanuele and Zuchowski, Lena (2025) Can We Test AI Like We Test Drugs? A Generative Analogy Between Machine Learning and Clinical Translation. [Preprint]
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Abstract
In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the basis of its epistemic warrants. In the literature, a parallel has been drawn between medicine and ML, suggesting that we should model epistemic standards for ML on the standards of clinical translation. By developing tools from Hesse’s (1966) work, we characterise the nature of this parallel as a generative analogy between the process of clinical translation and the process of building ML systems. We identify more precisely the epistemic warrants of clinical translation that are typically only mentioned when appealing to the analogy, and we show in which sense such warrants apply analogically to the context of ML. In particular, we interpret the epistemic warrants of clinical translation in reliabilist terms, and we show how this can inform a new form of ML reliabilism, which is compatible to existing reliabilist accounts in philosophy of AI.
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| Item Type: | Preprint | |||||||||
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| Keywords: | clinical translation; reliabilism; machine learning | |||||||||
| Subjects: | General Issues > Data Specific Sciences > Medicine > Clinical Trials General Issues > Evidence Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Technology |
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| Depositing User: | Dr Emanuele Ratti | |||||||||
| Date Deposited: | 22 Aug 2025 14:37 | |||||||||
| Last Modified: | 22 Aug 2025 14:37 | |||||||||
| Item ID: | 26335 | |||||||||
| Subjects: | General Issues > Data Specific Sciences > Medicine > Clinical Trials General Issues > Evidence Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Models and Idealization General Issues > Technology |
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| Date: | 2025 | |||||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/26335 |
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