Politics of data reuse in machine learning systems: Theorizing reuse entanglements
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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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Big Data and Society |
| Vol/bind | 9 |
| Udgave nummer | 2 |
| Antal sider | 10 |
| ISSN | 2053-9517 |
| DOI | |
| Status | Udgivet - 2022 |
Bibliografisk note
Funding Information:
The authors acknowledge the Independent Research Fund Denmark grant “AI REUSE” and the ERC Advanced Grant “Algorithmic Societies: Ethical Life in the Age of Machine Learning” for supporting their research. The article is indebted to ongoing dialogues about data sets and machine learning with research communities in Denmark, the UK and abroad, including the TechSoc Cluster (Copenhagen Business School), Digital Democracies Institute (Simon Fraser University), the AI Governance and Governmentality Seminar Series (Concordia University) and the participants in the Inference Worlds panel track (EASST 2022). We particularly thank the three anonymous reviewers for their excellent reflections as well as Daniela Agostinho, Robin Steedman, Frederik Schade and Daniel Hardt for their invaluable input on earlier iterations of this article. Finally, we would like to thank the editors of Big Data & Society for giving this article a home. The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Det Frie Forskningsråd, ERC Advanced Grant (grant numbers 9131-00115B and 883107-ALGOSOC).
Funding Information:
The authors acknowledge the Independent Research Fund Denmark grant “AI REUSE” and the ERC Advanced Grant “Algorithmic Societies: Ethical Life in the Age of Machine Learning” for supporting their research. The article is indebted to ongoing dialogues about data sets and machine learning with research communities in Denmark, the UK and abroad, including the TechSoc Cluster (Copenhagen Business School), Digital Democracies Institute (Simon Fraser University), the AI Governance and Governmentality Seminar Series (Concordia University) and the participants in the Inference Worlds panel track (EASST 2022). We particularly thank the three anonymous reviewers for their excellent reflections as well as Daniela Agostinho, Robin Steedman, Frederik Schade and Daniel Hardt for their invaluable input on earlier iterations of this article. Finally, we would like to thank the editors of Big Data & Society for giving this article a home.
Funding Information:
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Det Frie Forskningsråd, ERC Advanced Grant (grant numbers 9131-00115B and 883107-ALGOSOC).
Publisher Copyright:
© The Author(s) 2022.
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