Two Dimensions of Opacity and the Deep Learning Predicament

Minds and Machines 32 (1):43-75 (2021)
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Deep neural networks have become increasingly successful in applications from biology to cosmology to social science. Trained DNNs, moreover, correspond to models that ideally allow the prediction of new phenomena. Building in part on the literature on ‘eXplainable AI’, I here argue that these models are instrumental in a sense that makes them non-explanatory, and that their automated generation is opaque in a unique way. This combination implies the possibility of an unprecedented gap between discovery and explanation: When unsupervised models are successfully used in exploratory contexts, scientists face a whole new challenge in forming the concepts required for understanding underlying mechanisms.



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Florian J. Boge
Bergische Universität Wuppertal

References found in this work

Depth: An Account of Scientific Explanation.Michael Strevens - 2008 - Cambridge, Mass.: Harvard University Press.
Idealization and the Aims of Science.Angela Potochnik - 2017 - Chicago: University of Chicago Press.
Science in the age of computer simulation.Eric B. Winsberg - 2010 - Chicago: University of Chicago Press.
Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.
Scientific perspectivism.Ronald N. Giere - 2006 - Chicago: University of Chicago Press.

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