Wuthering Weights—Localisation Trajectories of Machine Learning Models for Local Ends
Engdahl, Isak LU
(2024)
In De Gruyter Handbooks of Digital Transformation
2.
p.321-336
- Abstract
- This chapter analyses trajectories of contextual reconstruction of machine learning models with calibrated weights, sometimes known as pre-trained or foundation models, as they are made operational in specific settings. Through ethnographic vignettes on car detection, bird tracking, and medical technology, it underscores the importance of cooperation and negotiation among actors facing contingencies during trajectories of contextual reconstruction. These stories help us see the behind-the-scenes work of bringing machine learning systems into existence, including interactions between machine learning scientists and domain members. Trajectories are shown to be non-linear, negotiated, and contingent on technical, infrastructural, and... (More)
- This chapter analyses trajectories of contextual reconstruction of machine learning models with calibrated weights, sometimes known as pre-trained or foundation models, as they are made operational in specific settings. Through ethnographic vignettes on car detection, bird tracking, and medical technology, it underscores the importance of cooperation and negotiation among actors facing contingencies during trajectories of contextual reconstruction. These stories help us see the behind-the-scenes work of bringing machine learning systems into existence, including interactions between machine learning scientists and domain members. Trajectories are shown to be non-linear, negotiated, and contingent on technical, infrastructural, and regulatory conditions. The process of localising such models results from the collective effort of actors, their negotiations, and resource combinations. Actors use their resources to gather expertise and pool work efforts to address unforeseen events during contextual reconstruction trajectories. The chapters thus considers the wielding of an implicit social license, whereby actors leverage their accreditations to successfully navigate contingencies during work with models that otherwise are open-source and retrievable for many actors. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/44d154ca-70ca-42c8-86fa-9020a328e808
- author
- Engdahl, Isak
LU
- organization
- publishing date
- 2024-09-09
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Science and Technology Studies (STS), machine learning, computer vision, ethnography, foundation models, object detection
- host publication
- The De Gruyter Handbook of Automated Futures: Imaginaries, Interactions and Impact
- series title
- De Gruyter Handbooks of Digital Transformation
- editor
- Berg, Martin ; Fors, Vaike and Brodersen, Meike
- volume
- 2
- pages
- 321 - 336
- publisher
- De Gruyter
- ISBN
- 9783110792249
- 9783110792256
- DOI
- 10.1515/9783110792256-020
- project
- Machine Pedagogy
- Show & Tell: Scientific representation, algorithmically generated visualizations, and evidence across epistemic cultures
- language
- English
- LU publication?
- yes
- id
- 44d154ca-70ca-42c8-86fa-9020a328e808
- date added to LUP
- 2025-05-20 20:15:43
- date last changed
- 2025-05-22 09:11:10
@inbook{44d154ca-70ca-42c8-86fa-9020a328e808,
abstract = {{This chapter analyses trajectories of contextual reconstruction of machine learning models with calibrated weights, sometimes known as pre-trained or foundation models, as they are made operational in specific settings. Through ethnographic vignettes on car detection, bird tracking, and medical technology, it underscores the importance of cooperation and negotiation among actors facing contingencies during trajectories of contextual reconstruction. These stories help us see the behind-the-scenes work of bringing machine learning systems into existence, including interactions between machine learning scientists and domain members. Trajectories are shown to be non-linear, negotiated, and contingent on technical, infrastructural, and regulatory conditions. The process of localising such models results from the collective effort of actors, their negotiations, and resource combinations. Actors use their resources to gather expertise and pool work efforts to address unforeseen events during contextual reconstruction trajectories. The chapters thus considers the wielding of an implicit social license, whereby actors leverage their accreditations to successfully navigate contingencies during work with models that otherwise are open-source and retrievable for many actors.}},
author = {{Engdahl, Isak}},
booktitle = {{The De Gruyter Handbook of Automated Futures: Imaginaries, Interactions and Impact}},
editor = {{Berg, Martin and Fors, Vaike and Brodersen, Meike}},
isbn = {{9783110792249}},
keywords = {{Science and Technology Studies (STS); machine learning; computer vision; ethnography; foundation models; object detection}},
language = {{eng}},
month = {{09}},
pages = {{321--336}},
publisher = {{De Gruyter}},
series = {{De Gruyter Handbooks of Digital Transformation}},
title = {{Wuthering Weights—Localisation Trajectories of Machine Learning Models for Local Ends}},
url = {{http://dx.doi.org/10.1515/9783110792256-020}},
doi = {{10.1515/9783110792256-020}},
volume = {{2}},
year = {{2024}},
}