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Künstliche Intelligenz: Eine Methode für alles? Sozialwissenschaftliche Methodologie der KI-Forschung, ihre Herausforderungen und Möglichkeiten

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Künstliche Intelligenz, Mensch und Gesellschaft

Zusammenfassung

Künstliche Intelligenz (KI) ist als Mittel oder Herausforderung der Forschung methodisch relevant. Unser Beitrag untersucht die Bandbreite methodischer KI Forschung über und mit KI aus sozialwissenschaftlicher Perspektive. Aus unseren digitalen und Grounded Theory Methoden und Kartierungen, ergibt sich Einsicht in die disziplinäre und wissenschaftspolitische Bandbreite der KI-Forschung, sowie ihr verbindendes Potenzial zwischen Disziplinen, Traditionen, und Themen. Ausgehend dieser diskursiv-reflexiven und kollektiv-bildenden Eigenschaft, argumentieren wir für eine Differenzierung von KI als Gegenstand und Mittel. Es ist genau zu unterscheiden, um welche Art, Form und Konzept von KI es sich handelt: Computational brute force, Expert system, Neural net, oder Dispositiv und Imaginäres – und inwiefern dabei auch konzeptionell idente Technologien vergleichbar sind. In Konsequenz gehen wir intensiv auf unsere eigene Methode, insbesondere die verwendeten Mapping-Algorithmen ein. Forschung und Lehre müssen sich für diese Unterschiede sensibilisieren, um KI-Technologien sinnvoll nutzen und untersuchen zu können.

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Author information

Authors and Affiliations

  1. Europa Universität Viadrina, Frankfurt (Oder), Deutschland

    Peter Kahlert, Suzette Kahlert, Silvan Pollozek, Johan Buchholz, Benedict Lang & Jan-Hendrik Passoth

  2. Technische Universität München, München, Deutschland

    Maryam Tatari

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  1. Peter Kahlert
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  7. Jan-Hendrik Passoth
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Correspondence to Peter Kahlert .

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Editors and Affiliations

  1. Institut für Sozialwissenschaftliche Forschung e.V. – ISF München, München, Deutschland

    Michael Heinlein

  2. Institut für Sozialwissenschaftliche Forschung e.V. – ISF München, München, Deutschland

    Norbert Huchler

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Kahlert, P. et al. (2024). Künstliche Intelligenz: Eine Methode für alles? Sozialwissenschaftliche Methodologie der KI-Forschung, ihre Herausforderungen und Möglichkeiten. In: Heinlein, M., Huchler, N. (eds) Künstliche Intelligenz, Mensch und Gesellschaft. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-43521-9_19

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  • DOI: https://doi.org/10.1007/978-3-658-43521-9_19

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