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      From rules to examples: Machine learning's type of authority

      1 , 2
      Big Data & Society
      SAGE Publications

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          Abstract

          This paper analyzes the effects of a perceived transition from a rule-based computer programming paradigm to an example-based paradigm associated with machine learning. While both paradigms coexist in practice, we critically discuss the distinctive epistemological and ethical implications of machine learning's “exemplary” type of authority. To capture its logic, we compare it to computer programming rules that date to the middle of the 20th century, showing how rules and examples have regulated human conduct in significantly different ways. In contrast to the highly constructed, explicit, and prescriptive form of authority imposed by programming rules, machine learning models are trained using data that has been made into examples. These examples elicit norms in an implicit, emergent manner to make prediction and classification possible. We analyze three ways that examples are produced in machine learning: labeling, feature engineering, and scaling. We use the phrase “artificial naturalism” to characterize the tensions of this type of authority, in which examples sit ambiguously between data and norm.

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          Most cited references41

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          ImageNet: A large-scale hierarchical image database

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            Representation learning: a review and new perspectives.

            The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
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              I.—COMPUTING MACHINERY AND INTELLIGENCE

              A Turing (1950)
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Big Data & Society
                Big Data & Society
                SAGE Publications
                2053-9517
                2053-9517
                July 2023
                September 13 2023
                July 2023
                : 10
                : 2
                Affiliations
                [1 ]Department of Geography, Durham University, Durham, United Kingdom
                [2 ]Institut für Medienwissenschaft, Ruhr-Universität Bochum, Bochum, Germany
                Article
                10.1177/20539517231188725
                fe2667cc-2d2f-4bad-b37d-755034c2d3c5
                © 2023

                https://creativecommons.org/licenses/by-nc-nd/4.0/

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