Ratti, Emanuele (2019) Phronesis and Automated Science: The Case of Machine Learning and Biology. [Preprint]
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Abstract
The applications of machine learning (ML) and deep learning to the natural sciences has fostered the idea that the automated nature of algorithmic analysis will gradually dispense human beings from scientific work. In this paper, I will show that this view is problematic, at least when ML is applied to biology. In particular, I will claim that ML is not independent of human beings and cannot form the basis of automated science. Computer scientists conceive their work as being a case of Aristotle’s poiesis perfected by techne, which can be reduced to a number of straightforward rules and technical knowledge. I will show a number of concrete cases where at each level of computational analysis, more is required to ML than just poiesis and techne, and that the work of ML practitioners in biology needs also the cultivation of something analogous to phronesis, which cannot be automated. But even if we knew how to frame phronesis into rules (which is inconsistent with its own definition), still this virtue is deeply entrenched in our biological constitution, which computers lack. Whether computers can fully perform scientific practice (which is the result of the way we are cognitively and biologically) independently of humans (and their cognitive and biological specificities) is an ill-posed question.
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| Item Type: | Preprint | ||||||
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| Keywords: | automated science; machine learning; experimental biology; phronesis | ||||||
| Subjects: | Specific Sciences > Biology Specific Sciences > Computation/Information Specific Sciences > Computer Science General Issues > Experimentation General Issues > Technology |
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| Depositing User: | Dr Emanuele Ratti | ||||||
| Date Deposited: | 25 Feb 2019 21:28 | ||||||
| Last Modified: | 25 Feb 2019 21:28 | ||||||
| Item ID: | 15770 | ||||||
| Subjects: | Specific Sciences > Biology Specific Sciences > Computation/Information Specific Sciences > Computer Science General Issues > Experimentation General Issues > Technology |
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| Date: | 2019 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/15770 |
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