Articulation Work and Tinkering for Fairness in Machine Learning

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dc.contributor.authorFahimi, Miriam
dc.contributor.authorRusso, Mayra
dc.contributor.authorScott, Kristen M.
dc.contributor.authorVidal, Maria-Esther
dc.contributor.authorBerendt, Bettina
dc.contributor.authorKinder-Kurlanda, Katharina
dc.date.accessioned2025-03-31T13:51:13Z
dc.date.available2025-03-31T13:51:13Z
dc.date.issued2024
dc.description.abstractThe 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.
dc.identifier.citationFahimi, M., Russo, M., Scott, K. M., Vidal, M.-E., Berendt, B., & Kinder-Kurlanda, K. (2024). Articulation Work and Tinkering for Fairness in Machine Learning. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2), 1–23. https://doi.org/10.1145/3686973
dc.identifier.doi10.1145/3686973
dc.identifier.issn2573-0142
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/855
dc.language.isoen
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectfair machine learning
dc.subjectarticulation work
dc.subjectdoability
dc.subjectinterview study
dc.titleArticulation Work and Tinkering for Fairness in Machine Learning
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.urlhttps://dl.acm.org/doi/10.1145/3686973
local.researchgroupDaten, algorithmische Systeme und Ethik
local.researchtopicDigitale Technologien in der Gesellschaft
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