Volltext herunterladen
(2.867 MB)
Zitationshinweis
Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgenden Persistent Identifier (PID):
https://doi.org/10.34669/wi.cp/4.5
Export für Ihre Literaturverwaltung
Machine Learning and the End of Theory: Reflections on a Data-Driven Conception of Health
[Konferenzbeitrag]
Dieser Sammelwerksbeitrag gehört zu folgendem Sammelwerk:
Proceedings of the Weizenbaum Conference 2022: Practicing Sovereignty - Interventions for Open Digital Futures
Körperschaftlicher Herausgeber
Weizenbaum Institute for the Networked Society - The German Internet Institute
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 solel... mehr
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.... weniger
Thesaurusschlagwörter
computerunterstütztes Lernen; Gesundheit; Technikfolgen; Digitalisierung; künstliche Intelligenz
Klassifikation
Technikfolgenabschätzung
Wissenschaftssoziologie, Wissenschaftsforschung, Technikforschung, Techniksoziologie
Freie Schlagwörter
machine learning; health theory; maschinelles Lernen
Titel Sammelwerk, Herausgeber- oder Konferenzband
Proceedings of the Weizenbaum Conference 2022: Practicing Sovereignty - Interventions for Open Digital Futures
Herausgeber
Herlo, Bianca; Irrgang, Daniel
Konferenz
4. Weizenbaum Conference "Practicing Sovereignty: Interventions for Open Digital Futures". Berlin, 2022
Sprache Dokument
Englisch
Publikationsjahr
2023
Erscheinungsort
Berlin
Seitenangabe
S. 53-65
ISSN
2510-7666
Status
Erstveröffentlichung; begutachtet