Why Are Human Epistemic Agents Not Displaced in Machine Learning Scientific Inquiries? : A Practice Perspective on ML in Science
| dc.contributor.author | Styger, Sahra A. | |
| dc.contributor.author | de Heer Kloots, Marianne | |
| dc.contributor.author | van der Wal, Oskar | |
| dc.contributor.author | Russo, Federica | |
| dc.date.accessioned | 2026-01-21T08:15:55Z | |
| dc.date.available | 2026-01-21T08:15:55Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | This chapter considers machine learning (ML) practices used in science. Because ML practices enjoy increasing degrees of automation at various stages of the process, the question whether human epistemic agents are displaced arises. We first point out that shifting focus from the ML outputs to the practice of designing and using ML models allows one to appreciate the role of different actors in this process, from the human designers and modelers to the algorithms themselves. We illustrate this point with a description of ML-based practices in neuroscience. We then go further with problematizing the role of human epistemic agents in ML and argue that they are not displaced. | |
| dc.description.version | published | deu |
| dc.identifier.doi | 10.1007/978-3-032-03083-2_15 | |
| dc.identifier.ppn | 1949837017 | |
| dc.identifier.uri | https://kops.uni-konstanz.de/handle/123456789/75791 | |
| dc.language.iso | eng | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Human epistemic agents | |
| dc.subject | Artificial agents | |
| dc.subject | ML practices | |
| dc.subject | NLP | |
| dc.subject | Cognitive neuroscience | |
| dc.subject | Algorithmic bias | |
| dc.subject | Human displacement | |
| dc.subject | Human-in-the-loop | |
| dc.subject.ddc | 100 | |
| dc.title | Why Are Human Epistemic Agents Not Displaced in Machine Learning Scientific Inquiries? : A Practice Perspective on ML in Science | eng |
| dc.type | INCOLLECTION | |
| dspace.entity.type | Publication | |
| kops.citation.bibtex | @incollection{Styger2026Human-75791,
title={Why Are Human Epistemic Agents Not Displaced in Machine Learning Scientific Inquiries? : A Practice Perspective on ML in Science},
year={2026},
doi={10.1007/978-3-032-03083-2_15},
number={527},
isbn={978-3-032-03082-5},
address={Cham},
publisher={Springer},
series={Synthese Library (SYLI)},
booktitle={Philosophy of Science for Machine Learning : Core Issues and New Perspectives},
pages={315--337},
editor={Durán, Juan M. and Pozzi, Giorgia},
author={Styger, Sahra A. and de Heer Kloots, Marianne and van der Wal, Oskar and Russo, Federica}
} | |
| kops.citation.iso690 | STYGER, Sahra A., Marianne DE HEER KLOOTS, Oskar VAN DER WAL, Federica RUSSO, 2026. Why Are Human Epistemic Agents Not Displaced in Machine Learning Scientific Inquiries? : A Practice Perspective on ML in Science. In: DURÁN, Juan M., Hrsg., Giorgia POZZI, Hrsg.. Philosophy of Science for Machine Learning : Core Issues and New Perspectives. Cham: Springer, 2026, S. 315-337. Synthese Library (SYLI). 527. ISBN 978-3-032-03082-5. Verfügbar unter: doi: 10.1007/978-3-032-03083-2_15 | deu |
| kops.citation.iso690 | STYGER, Sahra A., Marianne DE HEER KLOOTS, Oskar VAN DER WAL, Federica RUSSO, 2026. Why Are Human Epistemic Agents Not Displaced in Machine Learning Scientific Inquiries? : A Practice Perspective on ML in Science. In: DURÁN, Juan M., ed., Giorgia POZZI, ed.. Philosophy of Science for Machine Learning : Core Issues and New Perspectives. Cham: Springer, 2026, pp. 315-337. Synthese Library (SYLI). 527. ISBN 978-3-032-03082-5. Available under: doi: 10.1007/978-3-032-03083-2_15 | eng |
| kops.citation.rdf | <rdf:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/75791">
<bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/75791"/>
<dc:contributor>de Heer Kloots, Marianne</dc:contributor>
<dcterms:abstract>This chapter considers machine learning (ML) practices used in science. Because ML practices enjoy increasing degrees of automation at various stages of the process, the question whether human epistemic agents are displaced arises. We first point out that shifting focus from the ML outputs to the practice of designing and using ML models allows one to appreciate the role of different actors in this process, from the human designers and modelers to the algorithms themselves. We illustrate this point with a description of ML-based practices in neuroscience. We then go further with problematizing the role of human epistemic agents in ML and argue that they are not displaced.</dcterms:abstract>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2026-01-21T08:15:55Z</dc:date>
<dc:creator>de Heer Kloots, Marianne</dc:creator>
<dc:creator>Styger, Sahra A.</dc:creator>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/40"/>
<dcterms:title>Why Are Human Epistemic Agents Not Displaced in Machine Learning Scientific Inquiries? : A Practice Perspective on ML in Science</dcterms:title>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2026-01-21T08:15:55Z</dcterms:available>
<dc:rights>Attribution 4.0 International</dc:rights>
<dc:creator>Russo, Federica</dc:creator>
<dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/75791/4/Styger_2-6nbb3kdmcrxu5.pdf"/>
<dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/75791/4/Styger_2-6nbb3kdmcrxu5.pdf"/>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/40"/>
<dcterms:issued>2026</dcterms:issued>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dc:creator>van der Wal, Oskar</dc:creator>
<dc:contributor>Russo, Federica</dc:contributor>
<dc:contributor>van der Wal, Oskar</dc:contributor>
<dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
<dc:language>eng</dc:language>
<dc:contributor>Styger, Sahra A.</dc:contributor>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
</rdf:Description>
</rdf:RDF> | |
| kops.description.openAccess | openaccessbookpart | |
| kops.flag.knbibliography | false | |
| kops.identifier.nbn | urn:nbn:de:bsz:352-2-6nbb3kdmcrxu5 | |
| kops.sourcefield | DURÁN, Juan M., Hrsg., Giorgia POZZI, Hrsg.. <i>Philosophy of Science for Machine Learning : Core Issues and New Perspectives</i>. Cham: Springer, 2026, S. 315-337. Synthese Library (SYLI). 527. ISBN 978-3-032-03082-5. Verfügbar unter: doi: 10.1007/978-3-032-03083-2_15 | deu |
| kops.sourcefield.plain | DURÁN, Juan M., Hrsg., Giorgia POZZI, Hrsg.. Philosophy of Science for Machine Learning : Core Issues and New Perspectives. Cham: Springer, 2026, S. 315-337. Synthese Library (SYLI). 527. ISBN 978-3-032-03082-5. Verfügbar unter: doi: 10.1007/978-3-032-03083-2_15 | deu |
| kops.sourcefield.plain | DURÁN, Juan M., ed., Giorgia POZZI, ed.. Philosophy of Science for Machine Learning : Core Issues and New Perspectives. Cham: Springer, 2026, pp. 315-337. Synthese Library (SYLI). 527. ISBN 978-3-032-03082-5. Available under: doi: 10.1007/978-3-032-03083-2_15 | eng |
| relation.isAuthorOfPublication | 519917d8-e740-41c3-ad81-f66e4a6cac17 | |
| relation.isAuthorOfPublication.latestForDiscovery | 519917d8-e740-41c3-ad81-f66e4a6cac17 | |
| source.bibliographicInfo.fromPage | 315 | |
| source.bibliographicInfo.seriesNumber | 527 | |
| source.bibliographicInfo.toPage | 337 | |
| source.contributor.editor | Durán, Juan M. | |
| source.contributor.editor | Pozzi, Giorgia | |
| source.identifier.isbn | 978-3-032-03082-5 | |
| source.publisher | Springer | |
| source.publisher.location | Cham | |
| source.relation.ispartofseries | Synthese Library (SYLI) | |
| source.title | Philosophy of Science for Machine Learning : Core Issues and New Perspectives |
Dateien
Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- Styger_2-6nbb3kdmcrxu5.pdf
- Größe:
- 587.98 KB
- Format:
- Adobe Portable Document Format
Lizenzbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
- Name:
- license.txt
- Größe:
- 3.96 KB
- Format:
- Item-specific license agreed upon to submission
- Beschreibung:

