Making Arguments with Data: Resisting Appropriation and Assumption of Access / Reason in Machine Learning Training Processes

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Author (Corporation)
Publication date
30.10.2023
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Type
01A - Journal article
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Parent work
Weizenbaum Journal of the Digital Society
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DOI of the original publication
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Volume
3
Issue / Number
2
Pages / Duration
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Weizenbaum Institute for the Networked Society
Place of publication / Event location
Berlin
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Abstract
This article presents an approach to practicing ethics when working with large datasets and designing data representations. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that machine learning models can be navigated in a multi-narrative manner when access to training data is well articulated and understood. We programmed and used web-based interfaces to sort, organize, and explore a community-run digital archive of radio signals. An additional perspective on the question of working with datasets is offered from the experience of teaching image synthesis with freely accessible online tools. We hold that the main challenge to social transformations related to digital technologies comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. To counter both the unfounded narratives of techno-optimismand the universalizing critique of technology, we discuss an approachto data and networks that enables a situated critique of datafication and correlationism from within.
Keywords
ethics of digital tools, critical data studies, data observatories, assumption of access, situated knowledge, machine learning, artificial intelligence
Subject (DDC)
700 - Künste und Unterhaltung
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ISBN
ISSN
2748-5625
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Diamond
License
'https://creativecommons.org/licenses/by/4.0/'
Citation
SAVIC, Selena und Yann Patrick MARTINS, 2023. Making Arguments with Data: Resisting Appropriation and Assumption of Access / Reason in Machine Learning Training Processes. Weizenbaum Journal of the Digital Society. 30 Oktober 2023. Bd. 3, Nr. 2. DOI 10.34669/WI.WJDS/3.2.4. Verfügbar unter: https://doi.org/10.26041/fhnw-5663