Dr. rer. nat.

Hecker, Dirk

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  • Publication
    Developing trustworthy AI applications with foundation models
    ( 2024-04)
    Mock, Michael
    ;
    Schmidt, Sebastian
    ;
    Müller, Felix Benjamin
    ;
    Görge, Rebekka
    ;
    Schmitz, Anna
    ;
    Haedecke, Elena Gina orcid-logo
    ;
    Voß, Angelika
    ;
    Hecker, Dirk
    ;
    Poretschkin, Maximilian
    ;
    Kern, Carmen
    ;
    Loh, Silke
    The trustworthiness of AI applications has been the subject of recent research and is also addressed in the EU's recently adopted AI Regulation. The currently emerging foundation models in the field of text, speech and image processing offer completely new possibilities for developing AI applications. This whitepaper shows how the trustworthiness of an AI application developed with foundation models can be evaluated and ensured. For this purpose, the application-specific, risk-based approach for testing and ensuring the trustworthiness of AI applications, as developed in the "AI Assessment Catalog - Guideline for Trustworthy Artificial Intelligence" by Fraunhofer IAIS, is transferred to the context of foundation models. Special consideration is given to the fact that specific risks of foundation models can have an impact on the AI application and must also be taken into account when checking trustworthiness.