We study how to make AI systems transparent, robust, and aligned with people — from interpretable models and trustworthy language technology to applied machine learning in sensitive domains.

Our research areas

Explainable AI

Understanding models, generating explanations, and evaluating interpretability.

Language & LLMs

Capabilities, limitations, and behavior of large language models.

Applied Machine Learning

ML for high-stakes, safety-critical, and real-world domains.

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Recent news

Student Chatbot Marcel (EMNLP'25)

At this year’s EMNLP in Suzhou, China, Jan Trienes presented Marcel—a RAG-based conversational agent built to handle enrollment questions at Marburg University (Trienes et al., 2025). The project grew out of a simple ...

Student Initiative Receives Hessian Teaching Award

The AI-Lab: AI & Society project has been awarded the Hessian University Teaching Prize! This recognition reflects the passion, creativity, and hard work of everyone involved—especially the student leaders who tur...

Annotation-Free Breast Cancer Prediction (CBM)

Most AI models for breast cancer detection assume that each mammogram image, or even each region within an image, has been manually labeled. However, in real hospitals, clinicians only provide a final, case-level diag...

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Selected publications

(Le et al., 2025; Youssef et al., 2025; Trienes et al., 2025; Nauta et al., 2023; Papenmeier et al., 2022)

  1. Jan Trienes, Jörg Schlötterer, Junyi Jessy Li, and Christin Seifert. Behavioral Analysis of Information Salience in Large Language Models. Findings of the Association for Computational Linguistics: ACL 2025. 2025.
    BibTeX 
    @inproceedings{Trienes2025_acl_information-salience,
  author = {Trienes, Jan and Schl{\"o}tterer, J{\"o}rg and Li, Junyi Jessy and Seifert, Christin},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  title = {Behavioral Analysis of Information Salience in Large Language Models},
  year = {2025},
  address = {Vienna, Austria},
  editor = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
  month = jul,
  pages = {23428--23454},
  publisher = {Association for Computational Linguistics},
  code = {https://github.com/jantrienes/llm-salience},
  doi = {10.18653/v1/2025.findings-acl.1204},
  isbn = {979-8-89176-256-5},
  url = {https://aclanthology.org/2025.findings-acl.1204/}
}
  2. Paul Youssef, Zhixue Zhao, Christin Seifert, and Jörg Schlötterer. Has this Fact been Edited? Detecting Knowledge Edits in Language Models. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.
    BibTeX 
    @inproceedings{Youssef2025_naacl_detecting-knowledge-edits,
  author = {Youssef, Paul and Zhao, Zhixue and Seifert, Christin and Schl{\"o}tterer, J{\"o}rg},
  booktitle = {Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
  title = {Has this Fact been Edited? Detecting Knowledge Edits in Language Models},
  year = {2025},
  address = {Albuquerque, New Mexico},
  editor = {Chiruzzo, Luis and Ritter, Alan and Wang, Lu},
  month = apr,
  pages = {9768--9784},
  publisher = {Association for Computational Linguistics},
  code = {https://github.com/paulyoussef/deed},
  isbn = {979-8-89176-189-6},
  url = {https://aclanthology.org/2025.naacl-long.492/}
}
  3. Phuong Quynh Le, Jörg Schlötterer, and Christin Seifert. Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations. Transactions on Machine Learning Research. 2025.
    BibTeX 
    @article{Le2025_tmlr_spurious-correlations-wo-group-annotations,
  author = {Le, Phuong Quynh and Schl{\"o}tterer, J{\"o}rg and Seifert, Christin},
  journal = {Transactions on Machine Learning Research},
  title = {Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations},
  year = {2025},
  issn = {2835-8856},
  code = {https://github.com/aix-group/prusc},
  file = {:own-pdf/Le2025_tmlr_spurious-correlations-wo-group-annotations_publisher.pdf:PDF},
  url = {https://openreview.net/forum?id=EEeVYfXor5}
}
  4. Meike Nauta, Jörg Schlötterer, Maurice van Keulen, and Christin Seifert. PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023.
    BibTeX 
    @inproceedings{Nauta2023_cvpr_pipnet,
  author = {Nauta, Meike and Schl\"{o}tterer, J\"{o}rg and van Keulen, Maurice and Seifert, Christin},
  booktitle = {{IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
  title = {{PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification}},
  year = {2023},
  code = {https://github.com/M-Nauta/PIPNet},
  doi = {10.1109/CVPR52729.2023.00269},
  file = {:own-pdf/Nauta2023_cvpr_pipnet.pdf:PDF},
  url = {https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html}
}
  5. Andrea Papenmeier, Dagmar Kern, Daniel Hienert, Yvonne Kammerer, and Christin Seifert. How Accurate Does It Feel? – Human Perception of Different Types of Classification Mistakes. Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 2022.
    BibTeX 
    @inproceedings{Papenmeier2022a_chi_perceived-accuracy,
  author = {Papenmeier, Andrea and Kern, Dagmar and Hienert, Daniel and Kammerer, Yvonne and Seifert, Christin},
  booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems},
  title = {{How Accurate Does It Feel? -- Human Perception of Different Types of Classification Mistakes}},
  year = {2022},
  address = {New York, NY, USA},
  publisher = {Association for Computing Machinery},
  series = {CHI '22},
  doi = {10.1145/3491102.3501915},
  file = {:own-pdf/Papenmeier2022a_chi_percevied-accuracy_preprint.pdf:PDF}
}

All publications →