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Instrumental Perspectivism: Is AI Machine Learning Technology Like NMR Spectroscopy?

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A Critical Reflection on Automated Science

Part of the book series: Human Perspectives in Health Sciences and Technology ((HPHST,volume 1))

Abstract

The question, “Will science remain human?” expresses a worry that deep learning algorithms will replace scientists in making judgments of classification and inference and that something crucial will be lost if that happens. Ever since the introduction of telescopes and microscopes humans have relied on technologies to extend beyond human sensory perception in acquiring scientific knowledge. In this paper I explore whether the ways in which new learning technologies extend beyond human cognitive aspects of science can be treated instrumentally. I will consider the norms for determining the reliability of a detection instrument, nuclear magnetic resonance spectroscopy, in predicting models of protein atomic structure. Can the same norms that apply in that case be used to judge the reliability of Artificial Intelligence deep learning algorithms?

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Notes

  1. 1.

    AI, Machine Learning and Deep Learning are not identical. AI is a machine way of performing tasks that are characteristic of human cognition, but may or may not attempt to represent the way humans perform those tasks. Machine Learning is one set of practices to achieve AI, where the algorithm is not explicitly programmed to perform a task, but “learns” how to achieve a specified goal. Deep Learning is one form of Machine Learning that uses Artificial Neural Net structures, with many discrete layers (deep structure) of connected artificial neurons that implement a hierarchy of concepts.

  2. 2.

    See also Madden 1967, p. 387: “The incompleteness of science arises from the impossibility of describing every detail of nature, whether the universe be conceived as infinite or finite in space and time, and from the fact that any explanatory deductive system depends upon assumptions which are themselves not explained.”

  3. 3.

    See also Craver 2006 who appeals to the continuum between a mechanism sketch and an “ideally complete” (p. 360) description of a mechanism. Craver and Kaplan 2018 endorse the norm of Salmon-completess which judges comparative completeness of explanations (in contrast to models) in terms of fewer or more relevant details.

  4. 4.

    Craver and Kaplan 2018 refer to this as the more details are better view, which the also reject.

  5. 5.

    See Bogen and Woodward 1988 for an important distinction between data and phenomena.

  6. 6.

    There are different forms of theory ladenness. See Bogen’s 2017 SEP article distinction of perception loading, semantic theory loading, and salience. On Bogen’s classification, Duhem’s claim is about semantic theory loading.

  7. 7.

    See Glymour 1980 for other ways to manage the theory-ladenness of experimental observations.

  8. 8.

    See Buckner 2018 for an articulation of deep neural network AI processing as a form of transformation abstraction.

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Authors and Affiliations

  1. University of Pittsburgh, Department of History and Philosophy of Science, Pittsburgh, PA, USA

    Sandra D. Mitchell

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  1. Sandra D. Mitchell
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Correspondence to Sandra D. Mitchell .

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Editors and Affiliations

  1. Campus Bio-Medico University of Rome, Rome, Italy

    Marta Bertolaso

  2. Department of Philosophy, Sapienza University of Rome, Rome, Italy

    Fabio Sterpetti

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Mitchell, S.D. (2020). Instrumental Perspectivism: Is AI Machine Learning Technology Like NMR Spectroscopy?. In: Bertolaso, M., Sterpetti, F. (eds) A Critical Reflection on Automated Science. Human Perspectives in Health Sciences and Technology, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-25001-0_3

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