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L. Käde. Datenrecht und neue Technologien Nomos, Baden-Baden, 1 Edition, (2021)Die interdisziplinäre Analyse nimmt konkreten Bezug zu in derKI-Entwicklung eingesetzten Machine Learning (ML)-Frameworks und gibtpraxisrelevante Antworten auf damit zusammenhängendeurheberrechtliche Fragen. Insbesondere der Datenbank(werk)schutz fürML-Modelle steht dabei im Fokus. Die Arbeit bietet außerdem eineEinschätzung der Relevanz von Text und Data Mining-Schranken imKI-Kontext. Mit Blick auf die Erzeugung von Werken durch bzw. mithilfevon ML wird die Zurechnungsproblematik erörtert, eine Lösungvorgeschlagen und eine Hilfestellung zur Ermittlung eines Urhebersangeboten. Darüber hinaus erfolgt hinsichtlich etwaiger KI-Autonomieeine Einführung in die Zusammenhänge von Intelligenz, Kreativitätund Computational Creativity..
D. Iglezakis. Dataset, https://doi.org/10.15770/darus-471, (2020)Related Publication: Schembera, B. & Iglezakis, D. (2020). EngMeta - Metadata for Computational Engineering. International Journal of Metadata, Semantics and Ontologies, 7 (9). p-122-156. (doi:10.23455/ijmso-12345).
D. Admin, und D. Iglezakis. Dataset, https://doi.org/10.15770/darus-470, (2020)related publication: Related Publication: Iglezakis, D., Seeland, A. (2020). Titel von Publikation. In: Titel von Sammelband (2020).p. 11-22. (doi:10.18324/324392034).
A. Kesper, V. Wenz, und G. Taentzer. (2020)cite arxiv:2007.11298Comment: 28 pages. This paper is an extended version of a paper to be published in ÄCM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS '20)". Added subtitle.