The ethics and politics of data sets in the age of machine learning: deleting traces and encountering remains
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Individuals and communities increasingly depend on, and fill their lives with, machine cultures, in the form of both interfaces and infrastructures. This global push for machine cultures has given rise to an increasing demand for data and engendered a proliferation of public, private and public-private dataset repositories. While datasets form a foundational element of machine cultures, they rarely come into focus as objects of critical study. But in recent years a critical discursive formation on datasets has begun to emerge, which disturbs the idea of datasets as operational instruments of digital knowledge production and seek to instead ‘bring people back in’. The present article identifies these preliminary explorations as ‘critical dataset studies’ and draws on critical archival studies to articulate the ethico-political surfaced by these studies. Specifically it argues that critical dataset studies shows the need for an expanded ethical and conceptual approach to datasets that not only relies on linear notions of deletion and accountability but also on iterative frameworks of remains and response-ability.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Media, Culture and Society |
| Vol/bind | 44 |
| Udgave nummer | 4 |
| Sider (fra-til) | 655-671 |
| Antal sider | 17 |
| ISSN | 0163-4437 |
| DOI | |
| Status | Udgivet - 2022 |
Bibliografisk note
Funding Information:
The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article was made possible by the generosity of the Independent Research Fund Denmark, grant number 9131-00115B.
Funding Information:
It is indebted to ongoing generous dialogues about data sets, archives, residues and remains with research communities in Denmark and abroad, including the Digital Democracies Institute (Simon Fraser University), the Foundations of Machine Learning research group (an International Working Group at École Normale Supérieure), the AI Governance and Governmentality Seminar Series (Concordia University) and Data as Relation (IT University of Copenhagen). I particularly thank the anonymous reviewers as well as Taina Bucher, Daniela Agostinho, Kristin Veel and Thomas Gammeltoft-Hansen for their invaluable input and reflections on earlier iterations of this article. I would also like to express my gratitude for the intellectual generosity of members of AI Reuse research project (Louise Amoore, Kristian Bondo Hansen, Mikkel Flyverbom and Louis Ravn) and the Uncertain Archives research collective. I am also deeply indebted to Merl Storr for her diligent copy editing. Finally, I would like to thank the editors of this special issue for ensuring the publication of this special issue during difficult times and for bringing my own work into dialogue with authors whose work I deeply admire The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article was made possible by the generosity of the Independent Research Fund Denmark, grant number 9131-00115B.
Publisher Copyright:
© The Author(s) 2022.
ID: 356953449