At the Fraunhofer Institute FIT, we stand at the forefront of research in the field of generative AI and large language models. Our interdisciplinary research encompasses all application levels of generative AI. We delve deeply into understanding the complex requirements of these systems, ensuring they align with human-centric values and needs.

Our research addresses the evaluation of technical feasibility as well as the socio-ethical implications of generative AI. This dual approach ensures that our innovations not only expand technological boundaries but also meet societal norms and expectations.

Our commitment to excellence is reflected in our active participation in the scientific community. We regularly publish our work in renowned journals and conferences, sharing and discussing our findings with the research community. This continuous involvement helps us to further develop innovative ideas and stay abreast of the latest technology. At Fraunhofer FIT, we do not just observe digital transformation – we shape it.

Selection of relevant publications

  • Gimpel, H., Hall, K., Decker, S., Eymann, T., Lämmermann, L., Mädche, A., Röglinger, R., Ruiner, C., Schoch, M., Schoop, M., Urbach, N., Vandirk, S. (2023). Unlocking the Power of Generative AI Models and Systems such as GPT-4 and ChatGPT for Higher Education: A Guide for Students and Lecturers. University of Hohenheim, March 20, 2023.
  • Guggenberger, T., Lämmermann, L., Urbach, N., Walter, A. and Hofmann, P. (2023) Task delegation from AI to humans: A principal-agent perspective, Proceedings of the 44th International Conference on Information Systems, December 10-13, Hyderabad, India.
  • Duda, S., Hofmann, P., Urbach, N., Völter, F. and Zwickel, A. (2023) The Impact of Resource Allocation on the Machine Learning Lifecycle: Bridging the Gap between Software Engineering and Management, Business & Information Systems Engineering (BISE), forthcoming.
  • Hofmann, P., Jöhnk, J., Protschky, D. and Urbach, N. (2020) Developing Purposeful AI Use Cases – A Structured Method and its Application in Project Management, Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI 2020), March 9-11, Potsdam, Germany
  • Hofmann, P., Lämmermann, L., & Urbach, N. (2024). Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders. International Journal of Information Management, 75, 102728.
  • Kecht, C., Egger, A., Kratsch, W., & Röglinger, M. (2021). Event log construction from customer service conversations using natural language inference. In 2021 3rd International Conference on Process Mining (ICPM) (pp. 144-151). IEEE.
  • Bayer, S., Gimpel, H., Markgraf, M. (2021). The role of domain expertise in trusting and following explainable AI decision support systems. Journal of Decision Systems, 32(1):110-138.
  • Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., ... & Söllner, M. (2019). AI-based digital assistants: Opportunities, threats, and research perspectives. Business & Information Systems Engineering, 61, 535-544.