Dr Benjamin Jacobsen benjamin.jacobsen@durham.ac.uk
Post Doctoral Research Associate
Machine learning and the politics of synthetic data
Jacobsen, Benjamin N
Authors
Abstract
Machine-learning algorithms have become deeply embedded in contemporary society. As such, ample attention has been paid to the contents, biases, and underlying assumptions of the training datasets that many algorithmic models are trained on. Yet, what happens when algorithms are trained on data that are not real, but instead data that are ‘synthetic’, not referring to real persons, objects, or events? Increasingly, synthetic data are being incorporated into the training of machine-learning algorithms for use in various societal domains. There is currently little understanding, however, of the role played by and the ethicopolitical implications of synthetic training data for machine-learning algorithms. In this article, I explore the politics of synthetic data through two central aspects: first, synthetic data promise to emerge as a rich source of exposure to variability for the algorithm. Second, the paper explores how synthetic data promise to place algorithms beyond the realm of risk. I propose that an analysis of these two areas will help us better understand the ways in which machine-learning algorithms are envisioned in the light of synthetic data, but also how synthetic training data actively reconfigure the conditions of possibility for machine learning in contemporary society.
Citation
Jacobsen, B. N. (2023). Machine learning and the politics of synthetic data. Big Data and Society, 10(1), https://doi.org/10.1177/20539517221145372
Journal Article Type | Article |
---|---|
Online Publication Date | Jan 17, 2023 |
Publication Date | 2023 |
Deposit Date | Feb 22, 2023 |
Publicly Available Date | Feb 22, 2023 |
Journal | Big Data & Society |
Electronic ISSN | 2053-9517 |
Publisher | SAGE Publications |
Peer Reviewed | Peer Reviewed |
Volume | 10 |
Issue | 1 |
DOI | https://doi.org/10.1177/20539517221145372 |
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Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
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