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Machine learning, misinformation, and citizen science

  • Paper in the Philosophy of the Social Sciences and Humanities
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Abstract

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens’ and social scientists’ concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

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Notes

  1. See Murphy (2022) for details on these methods.

  2. This is not to be confused with how the workflow ought to be constructed; as I will argue later, there are many issues with the procedure as it is typically practiced.

  3. See Franklin (2016, 229-240) for a discussion of how varying thresholds for statistical significance have even decided the very ontology of sub-atomic particles.

  4. See Brockwell and Davis (2016, 13) for a precise mathematical definition. In essence, the behavior of stationary systems have stable statistical properties in its first and second moments for any given time-lag shift of that time series.

  5. See Hecker et al. (2018) for an overview of recent literature on global citizen science initiatives and their philosophies.

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Acknowledgements

I thank the following for constructive feedback on ideas in this paper: Brian Baigrie, 921 Franz Huber, Michael Miller, Regina Rini, Denis Walsh, the Pittsburgh HPS fringe theory group, the York 922 University moral psychology lab, four anonymous reviewers, the Hong Kong Catastrophic Risk Centre for funding, and the Philosophy of Contemporary and Future Science research group at Lingnan University, Department of Philosophy. All errors and infelicities are mine alone.

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  1. Department of Philosophy, Hong Kong Catastrophic Risk Centre, Lingnan University, Tuen Mun, Hong Kong

    Adrian K. Yee

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Yee, A.K. Machine learning, misinformation, and citizen science. Euro Jnl Phil Sci 13, 56 (2023). https://doi.org/10.1007/s13194-023-00558-1

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