Machine learning, misinformation, and citizen science

Adrian K. YEE

doi:10.1007/s13194-023-00558-1

Machine learning, misinformation, and citizen science

Adrian K. YEE^*

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

1 Citation (Scopus)

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.

Original language	English
Article number	56
Number of pages	24
Journal	European Journal for Philosophy of Science
Volume	13
Issue number	4
Early online date	22 Nov 2023
DOIs	https://doi.org/10.1007/s13194-023-00558-1
Publication status	Published - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, Springer Nature B.V.

Keywords

Citizen science
Construct validity
Machine learning
Measurement
Misinformation
Social Epistemology

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https://link.springer.com/10.1007/s13194-023-00558-1

Cite this

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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{\textquoteright} and social scientists{\textquoteright} 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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AB - 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.

KW - Citizen science

KW - Construct validity

KW - Machine learning

KW - Measurement

KW - Misinformation

KW - Social Epistemology

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ER -

Machine learning, misinformation, and citizen science

Abstract

Bibliographical note

Keywords

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