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The Impact of the EU AI Act’s Transparency Requirements on AI Innovation

In: Artificial Intelligence, Data, and Decision-Making

Author

Listed:
  • Luca Holst

    (University of Bayreuth)

  • Valentin Mayer

    (University of Bayreuth
    Branch Business & Information Systems, Engineering of the Fraunhofer FIT
    FIM Research Center)

  • Luis Lämmermann

    (Branch Business & Information Systems, Engineering of the Fraunhofer FIT
    FIM Research Center
    Frankfurt University of Applied Science)

  • Nils Urbach

    (Branch Business & Information Systems, Engineering of the Fraunhofer FIT
    FIM Research Center
    Frankfurt University of Applied Science)

  • Domenik Wendt

    (Frankfurt University of Applied Science)

Abstract

The increasing capabilities of Artificial Intelligence (AI) raise concerns about the risks associated with the technology. The European Union, therefore, proposed the Artificial Intelligence Act aiming to mitigate the risks of AI by fostering their safety and transparency. However, there is controversial debate about its impact on AI innovation. While the AI Act aims to provide legal certainty guiding innovation, the criticism refers to exaggerated bureaucratic burden such as transparency requirements impeding innovation. Based on a multivocal literature review, we examine the impact of the AI Act’s transparency requirements on patenting as a means for AI innovation. Our results indicate that the transparency requirements do not necessarily hinder the patentability of AI innovations. Instead, existing concerns primarily rely on uncertainties within key terms of the AI Act. Accordingly, we propose an improvement suggestion focusing on resolving existing uncertainties.

Suggested Citation

  • Luca Holst & Valentin Mayer & Luis Lämmermann & Nils Urbach & Domenik Wendt, 2026. "The Impact of the EU AI Act’s Transparency Requirements on AI Innovation," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Artificial Intelligence, Data, and Decision-Making, pages 135-151, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08480-4_10
    DOI: 10.1007/978-3-032-08480-4_10
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