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Interdisciplinary Colloquium on Digital Transformation of Research: "AI Training for Scientific Research: A Copyright Perspective" 02.04.2026 with Pascal Sierek

Authors: Sierek, Pascal T.;

Interdisciplinary Colloquium on Digital Transformation of Research: "AI Training for Scientific Research: A Copyright Perspective" 02.04.2026 with Pascal Sierek

Abstract

This presentation explores how European and German copyright law apply when AI systems are trained on large datasets that may include protected works. While such uses can amount to copyright infringement, the German text and data mining exceptions in §§ 44b and 60d UrhG offer important — but contested — legal bases for automated data processing. It outlines, when AI training may qualify as (scientific) text and data mining, highlights current debates in scholarship and case law, and discusses what researchers should know to avoid copyright risks. It was held in context of the Interdisciplinary Colloquium on Digitalisation of Research of the Leibniz Science Campus "DiTraRe" (=Digital Transformation of Research)

Keywords

Artificial Intelligence, Copyright

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green