Machine Learning and the End of Theory: Reflections on a Data-Driven Conception of Health

Guersenzvaig, Ariel

Machine Learning and the End of Theory: Reflections on a Data-Driven Conception of Health

dc.contributor.author	Guersenzvaig, Ariel
dc.date.accessioned	2023-08-29T12:09:28Z
dc.date.available	2023-08-29T12:09:28Z
dc.date.issued	2023
dc.description.abstract	Taking the notion of health as a leitmotif, this paper discusses some conceptual boundaries for using machine learning⁠ - a data-driven, statistical, and computational technique in the field of artificial intelligence⁠ - for epistemic purposes and for generating knowledge about the world based solely on the statistical correlations found in data (i.e., the "End of Theory" view⁠).The thrust of the argument is that prior theoretical conceptions, subjectivity, and values would - because of their normative power⁠ - inevitably blight any effort at knowledge-making that seeks to be exclusively driven by data and nothing else. The conclusion suggests that machine learning will neither resolve nor mitigate⁠ the serious internal contradictions found in the "biostatistical theory" of health⁠ - the most well-discussed data-driven theory of health. The definition of notions such as these is an ongoing and fraught societal dialogue where the discussion is not only about what is, but also about what should be. This dialogical engagement is a question of ethics and politics ⁠and not one of mathematics.	en
dc.description.sponsorship	This work has been funded by the Federal Ministry of Education and Research of Germany (BMBF) (grant no.: 16DII111, 16DII112, 16DII113, 16DII114, 16DII115, 16DII116, 16DII117 – „Deutsches Internet-Institut“)
dc.identifier.citation	Guersenzvaig, A. (2023). Machine Learning and the End of Theory: Reflections on a Data-Driven Conception of Health. Proceedings of the Weizenbaum Conference 2022: Practicing Sovereignty, 53–65. https://doi.org/10.34669/WI.CP/4.5
dc.identifier.doi	https://doi.org/10.34669/wi.cp/4.5
dc.identifier.eissn	2510-7666
dc.identifier.uri	https://www.weizenbaum-library.de/handle/id/76
dc.language.iso	eng
dc.publisher	Weizenbaum Institute
dc.relation.ispartof	https://doi.org/10.34669/WI.CP/4
dc.relation.ispartofseries	Weizenbaum Conference Proceedings
dc.rights	open access
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Technology (Applied sciences)	en
dc.subject	Sociology & anthropology	en
dc.subject	Technology Assessment	en
dc.subject	Sociology of Science, Sociology of Technology, Research on Science and Technology	en
dc.subject	computer aided learning	en
dc.subject	health	en
dc.subject	effects of technology	en
dc.subject	digitalization	en
dc.subject	artificial intelligence	en
dc.subject	Technik, Technologie	de
dc.subject	Soziologie, Anthropologie	de
dc.subject	Machine Learning	de
dc.subject	health theory	de
dc.subject	maschinelles Lernen	de
dc.subject	Technikfolgenabschätzung	de
dc.subject	Wissenschaftssoziologie, Wissenschaftsforschung, Technikforschung, Techniksoziologie	de
dc.subject	computerunterstütztes Lernen	de
dc.subject	Gesundheit	de
dc.subject	Technikfolgen	de
dc.subject	Digitalisierung	de
dc.subject	künstliche Intelligenz	de
dc.subject.ddc	600 Technik
dc.subject.ddc	300 Sozialwissenschaften
dc.title	Machine Learning and the End of Theory: Reflections on a Data-Driven Conception of Health
dc.type	ConferencePaper
dc.type.status	publishedVersion
dcmi.type	Text
dcterms.bibliographicCitation.booktitle	Proceedings of the Weizenbaum Conference 2022
dcterms.bibliographicCitation.originalpublisherplace	Berlin
dcterms.bibliographicCitation.pageend	65
dcterms.bibliographicCitation.pagestart	53