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Deep Learning in Science: Is there a Reason for (Philosophical) Pessimism?

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  • Martin Justin

    (University of Ljubljana, Faculty of Arts, Department of Philosophy, Ljubljana, Slovenia)

Abstract

In this article, I will review existing arguments for and against this philosophical pessimism about using deep learning models in science. Despite the remarkable results achieved by deep learning models networks in various scientific fields, some philosophers worry that because of their opacity, using these systems cannot improve our understanding of the phenomena studied. First, some terminological and conceptual clarification is provided. Then, I present a case for optimism, arguing that using opaque models does not hinder the possibility of gaining new understanding. After that, I present a critique of this argument. Finally, I present a case for pessimism, concluding that there are reasons to be pessimistic about the ability of deep learning models to provide us with new understanding of phenomena, studied by scientists.

Suggested Citation

  • Martin Justin, 2024. "Deep Learning in Science: Is there a Reason for (Philosophical) Pessimism?," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 22(1), pages 59-70.
    • Handle: RePEc:zna:indecs:v:22:y:2024:i:1:p:59-70
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    Keywords

    deep learning; scientific understanding; explanation; black box problem; artificial neural networks;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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