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Politics of data reuse in machine learning systems: Theorizing reuse entanglements

Thylstrup, Nanna Bonde; Hansen, Kristian Bondo; Flyverbom, Mikkel; Amoore, Louise

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Nanna Bonde Thylstrup

Kristian Bondo Hansen

Mikkel Flyverbom


Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well as emergent forms of regulation that seek to set demands for corporate conduct and the protection of civic rights. And the Protective measures include methods of traceability and assessments of ‘good’ and ‘bad’ datasets and algorithms that are considered to be traceable, stable and contained. However, these ways of thinking about both technology and ethics obscure a fundamental issue, namely that machine learning systems entangle data, algorithms and more-than-human environments in ways that challenge a well-defined separation. This article investigates the fundamental fallacy of most data reuse strategies as well as their regulation and mitigation strategies that data can somehow be followed, contained and controlled in machine learning processes. Instead, the article argues that we need to understand the reuse of data as an inherently entangled phenomenon. To examine this tension between the discursive regimes and the realities of data reuse, we advance the notion of reuse entanglements as an analytical lens. The main contribution of the article is the conceptualization of reuse that places entanglements at its core and the articulation of its relevance using empirical illustrations. This is important, we argue, for our understanding of the nature of data and algorithms, for the practical uses of data and algorithms and our attitudes regarding ethics, responsibility and regulation.


Thylstrup, N. B., Hansen, K. B., Flyverbom, M., & Amoore, L. (2022). Politics of data reuse in machine learning systems: Theorizing reuse entanglements. Big Data and Society, 9(2),

Journal Article Type Article
Online Publication Date Dec 13, 2022
Publication Date 2022
Deposit Date Apr 3, 2023
Publicly Available Date Apr 3, 2023
Journal Big Data & Society
Electronic ISSN 2053-9517
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 9
Issue 2


Published Journal Article (706 Kb)

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Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License ( which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (

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