Misaligned Behaviors and Dangerous Capabilities in AI Safety: A Conceptual Cross-Source Analysis
Alismail, Ahmad; Woldai, Betiel; Lanquillon, Carsten; Schacht, Sigurd (2026)
AI Transparency Conference (AITC) 2026.
Open Access Peer Reviewed
From Recognition to Refusal: Mapping Safety Formation Zones Across Model Architectures
Lanquillon, Carsten; Schacht, Sigurd (2026)
AI Transparency Conference (AITC) 2026.
Open Access Peer Reviewed
Rethinking Red Teaming: There Is More To Agents Than LLMs
Wacker, Thomas; Lanquillon, Carsten; Schacht, Sigurd (2026)
AI Transparency Conference (AITC) 2026.
Open Access Peer Reviewed
Architecture as Governance: Why LLM Agents Need Structural Compliance Enforcement
Höpfner, Steffen; Schacht, Sigurd (2026)
AI Transparency Conference (AITC) 2026.
Peer Reviewed
eDIF: A European Deep Inference Fabric for Remote Interpretability of LLM
Heithoff, Irma; Guggenberger, Marc; Kalogiannis, Sandra; Susanne, Mayer; Maag, Fabian; Schacht, Sigurd; Lanquillon, Carsten (2025)
Heithoff, Irma; Guggenberger, Marc; Kalogiannis, Sandra; Susanne, Mayer; Maag, Fabian...
arXiv, 2508.10553.
DOI: 10.48550/arXiv.2508.10553
Open Access
“Which vocational training program is best for me?” – Design of a recommender system for school students using large language models
Piazza, Alexander; Schacht, Sigurd; Herzog, Michael (2025)
UMAP Adjunct '25: Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, 425-428.
DOI: 10.1145/3708319.3733809
Peer Reviewed
School students need to make decisions about their career paths after graduating. In Germany, students can choose between more than 300 vocational training programs, which can be overwhelming. Frequently, the students hesitate to talk with career counselors. The objective of this research is, therefore, to provide a recommendation system for school students to support their decision-making, which is based on their interests and provides recommendations with explanations based on a LLM. This system was developed with a social robot as the user interface to make it easy to use and appeal to the young target group. Based on user observations, preliminary findings indicate that the system is a valuable and engaging approach to support career counseling activities.
Mechanistic Exploration of the Architectural Impact of DPO Fine-Tuning on Ethical Alignment in LLMs
Maag, Fabian; Woldai, Betiel; Schacht, Sigurd (2025)
In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2025. Lecture Notes in Computer Science, Springer, Cham 15820.
DOI: 10.1007/978-3-031-93415-5_3
Peer Reviewed
Eine Zeitreise mit der MariusKI – Generative KI macht den Ansbacher Hofastronomen erlebbar
Leich, Pierre; Schacht, Sigurd; Woldai, Betiel (2025)
Regiomontanusbote 38 (4), 10-13.
Open Access
Deception in LLMs: Self-Preservation and Autonomous Goals in Large Language Models
Kamath Barkur, Sudarshan; Schacht, Sigurd; Scholl, Johannes (2025)
arXiv, 2501.16513.
DOI: 10.48550/arXiv.2501.16513
Open Access
Inference Optimizations for Large Language Models: Effects, Challenges, and Practical Considerations
Donisch, Leo; Schacht, Sigurd; Lanquillon, Carsten (2024)
Arxiv.
DOI: 10.48550/arXiv.2408.03130
Open Access
An approach to optimize inference of the DIART speaker diarization pipeline
Aperdannier, Roman; Schacht, Sigurd; Piazza, Alexander (2024)
Arxiv.
DOI: 10.48550/arXiv.2408.02341
Open Access
Evaluierung von LLMs und Autonomen Agenten
Kamath Barkur, Sudarshan; Sitapara, Pratik; Leuschner, Sven; Schacht, Sigurd (2024)
In: Gollisch, S., Gröner, P. (eds): Ansbacher Kaleidoskop 2024, Festschrift zum 60. Geburtstag von Prof. Dr. Ute Ambrosius und Prof. Dr. Barbara Hedderich, Shaker Verlag, Düren, 35 - 54.
Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency
Aperdannier, Roman; Schacht, Sigurd; Piazza, Alexander (2024)
Arxiv.
DOI: 10.48550/arXiv.2407.04293
Open Access
A Review of Common Online Speaker Diarization Methods
Aperdannier, Roman; Schacht, Sigurd; Piazza, Alexander (2024)
Arxiv.
DOI: 10.48550/arXiv.2406.14464
Open Access
KI-basierte Sprachmodelle in der Lehre: Question-Generation-Modelle zur Messung des Lernfortschrittes von Studierenden
Woldai, Betiel; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)
Neues Handbuch Hochschullehre - Sonderausgabe zur TURN23.
Open Access
Systematic Evaluation of Different Approaches on Embedding Search
Aperdannier, Roman; Köppel, Melanie; Unger, Tamina; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)
Aperdannier, Roman; Köppel, Melanie; Unger, Tamina; Schacht, Sigurd...
In: Arai, K. (eds) Advances in Information and Communication. FICC 2024. Lecture Notes in Networks and Systems, Springer, Cham 920, 526–536.
DOI: 10.1007/978-3-031-53963-3_36
Peer Reviewed
Magenta: Metrics and Evaluation Framework for Generative Agents based on LLMs
Kamath Barkur, Sudarshan; Schacht, Sigurd (2024)
AHFE International, Intelligent Human Systems Integration: Integrating People and Intelligent Systems 119, 144–153.
DOI: 10.54941/ahfe1004478
Open Access Peer Reviewed
PHOENIX: Open-Source Language Adaption for Direct Preference Optimization
Uhlig, Matthias; Schacht, Sigurd; Kamath Barkur, Sudarshan (2024)
Arxiv.
DOI: 10.48550/arXiv.2401.10580
Open Access
The Social Media Hate Speech Barometer: Making of
Sauer, Sebastian; Piazza, Alexander; Schacht, Sigurd (2023)
5th International Conference Business Meets Technology, Valencia, Spain, 143-162.
DOI: 10.4995/BMT2023.2023.16724
Open Access Peer Reviewed
Hate speech, particularly on social media channels, is a pressing cybersecurity concern and can even threaten the very foundations of societal stability. While there is a growing body of literature on how to detect and mitigate hate speech, applied researchers lack a state-of-the-art yet easily accessible infrastructure to build their own hate speech detection pipelines. We aim to provide an example of such an infrastructure that can serve as a template for other researchers. The infrastructure we present is based on the latest machine learning technologies available in the R environment: The Tidymodels framework and its extension Tidytext, plus the Targets project management approach, are the building blocks of our proposed infrastructure. In short, our data pipeline starts with downloading and preprocessing tweets, using various methods to convert text into numerical information. We then apply state-of-the-art supervised machine learning pipelines, drawing on a range of learning algorithms and incorporating new tuning capabilities. The focus of this paper is to explain the setup and rationale of the infrastructure. Our infrastructure is freely available on Github at https://github.com/sebastiansauer/hate-speech-barometer.
A Qualitative Evaluation of an AI-Based Study Progress Forecast
Kamath Barkur, Sudarshan; Fersch, Mascha-Lea; Henne, Sophie; Schacht, Sigurd; Woldai, Betiel (2023)
Kamath Barkur, Sudarshan; Fersch, Mascha-Lea; Henne, Sophie; Schacht, Sigurd...
Artificial Intelligence in Education Technologies: New Development and Innovative Practices. 190, 3-13.
DOI: 10.1007/978-981-99-7947-9_1
Peer Reviewed
This paper presents the development and evaluation of a first prototype of an ai-based study progress forecast. This service is integrated within a conversational agent and can be used by students to show them their current study progress. First, implications for the set-up of a forecast application from the literature are described. Based on the requirements identified in the literature and from the project itself, a lightweight formula was created that enables calculating the remaining study time. In order to assess preliminary feasibility and perception of the model prototype, a qualitative focus group discussion was conducted with five participants. Overall, the study progress forecast was well received by the participants, especially the offer itself as well as the promptness of the service were highlighted.
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Prof. Dr. Sigurd Schacht
Hochschule Ansbach
Fakultät Wirtschaft
Technologietransferzentrum Neustadt a.d. Aisch - Secure & Smart Data & Process Management
Residenzstr. 8
91522 Ansbach
sigurd.schacht[at]hs-ansbach.de
ORCID iD: 0000-0002-1161-4724