CV

This side is still under construction!

Education

• B.Sc. in Psychology in IT, Technische Universität Darmstadt, 2015
• M.Sc. in Psychology in IT, Technische Universität Darmstadt, 2018

Current Position

• Since Jan 2019: Research Assistant at German Aerospace Center (DLR e.V.) in the Group for Intelligent Software Systems at the Institute for Softwaretechnology (Cologne and Sankt Augustin)
• PhD Candidate at the AIML group at TU Darmstadt

Publications

Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices

Jentzsch, S., Schramowski, P., Rothkopf, C., & Kersting, K. (2019, January). "Semantics derived automatically from language corpora contain human-like moral choices."" In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 37-44).

Conversational Interfaces for Explainable AI: A Human-Centred Approach

Jentzsch, S. F., Höhn, S., & Hochgeschwender, N. (2019, May). "Conversational interfaces for explainable AI: a human-centred approach." In International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems (pp. 77-92). Springer, Cham.

BERT has a Moral Compass: Improvements of ethical and moral values of machines

The Moral Choice Machine

Schramowski et al. (2010). "The moral choice machine." Frontiers in Artificial Intelligence. 3, 36.

A qualitative study of Machine Learning practices and engineering challenges in Earth Observation

Jentzsch et al. (2021). A qualitative study of Machine Learning practices and engineering challenges in Earth Observation. it-Information Technology, 63(4), 235-247.

Gender Bias in BERT – Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task

Jentzsch et al. (2022). Gender Bias in BERT-Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task. In Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP) (pp. 184-199). Association for Computational Linguistics.

Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work

Theis, S., Jentzsch, S., Deligiannaki, F., Berro, C., Raulf, A. P., & Bruder, C. (2023, July). Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work. In International Conference on Human-Computer Interaction (pp. 355-380). Cham: Springer Nature Switzerland.

ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models

Jentzsch, S., & Kersting, K. (2023). ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models. In Proceedings of the 14th Workshop in Computational Approaches to Subjectivity and Sentiment Analysis. Association for Computational Linguistics, USA.</p> </li> </article> </div>

Style Vectors for Steering Generative Large Language Model

Konen, K., Jentzsch, S., Diallo, D., Schütt, P., Bensch, O., El Baff, R., Opitz, D. & Hecking, T. (2024). Style Vectors for Steering Generative Large Language Model. arXiv e-prints, arXiv-2402.