Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 434 |
| Seitenumfang | 23 |
| Fachzeitschrift | Proceedings of the ACM on Human-Computer Interaction |
| Jahrgang | 8 |
| Ausgabenummer | CSCW2 |
| Publikationsstatus | Veröffentlicht - 8 Nov. 2024 |
Abstract
The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of ‘organizational alignment’: what makes research ‘doable’ is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.
ASJC Scopus Sachgebiete
- Sozialwissenschaften (insg.)
- Sozialwissenschaften (sonstige)
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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in: Proceedings of the ACM on Human-Computer Interaction, Jahrgang 8, Nr. CSCW2, 434, 08.11.2024.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Articulation Work and Tinkering for Fairness in Machine Learning
AU - Fahimi, Miriam
AU - Russo, Mayra
AU - Scott, Kristen M.
AU - Vidal, Maria Esther
AU - Berendt, Bettina
AU - Kinder-Kurlanda, Katharina
N1 - Publisher Copyright: © 2024 Copyright held by the owner/author(s).
PY - 2024/11/8
Y1 - 2024/11/8
N2 - The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of ‘organizational alignment’: what makes research ‘doable’ is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.
AB - The field of fair AI aims to counter biased algorithms through computational modelling. However, it faces increasing criticism for perpetuating the use of overly technical and reductionist methods. As a result, novel approaches appear in the field to address more socially-oriented and interdisciplinary (SOI) perspectives on fair AI. In this paper, we take this dynamic as the starting point to study the tension between computer science (CS) and SOI research. By drawing on STS and CSCW theory, we position fair AI research as a matter of ‘organizational alignment’: what makes research ‘doable’ is the successful alignment of three levels of work organization (the social world, the laboratory, and the experiment). Based on qualitative interviews with CS researchers, we analyze the tasks, resources, and actors required for doable research in the case of fair AI. We find that CS researchers engage with SOI research to some extent, but organizational conditions, articulation work, and ambiguities of the social world constrain the doability of SOI research for them. Based on our findings, we identify and discuss problems for aligning CS and SOI as fair AI continues to evolve.
KW - articulation work
KW - doability
KW - fair machine learning
KW - interview study
UR - http://www.scopus.com/inward/record.url?scp=85209543397&partnerID=8YFLogxK
U2 - 10.1145/3686973
DO - 10.1145/3686973
M3 - Conference article
AN - SCOPUS:85209543397
VL - 8
JO - Proceedings of the ACM on Human-Computer Interaction
JF - Proceedings of the ACM on Human-Computer Interaction
IS - CSCW2
M1 - 434
ER -