WSTNet International Seminar Speaker Series Webinar | 11 September 2026
WSTNet International Seminar Speaker Series Webinar
11 September 2026 | 2:00–3:30 pm CET
WSTNet Distinguished Talk
Artificial Intelligence Compliance – A Transdisciplinary Challenge
Abstract
Compliance means conforming to a set of rules, requirements, or standards that apply to an organization or individual. AI compliance is often discussed as a technical challenge to be achieved algorithmically. In contrast, we draw on other compliance challenges, e.g., financial compliance, and sketch a landscape in which many stakeholders from diverse backgrounds and disciplines must collaborate to achieve AI compliance throughout the lifecycle of an AI system.
Prof. Steffen Staab
University of Stuttgart, Germany, and University of Southampton, UK
Speaker bio
Steffen is a professor for Analytic Computing and heads the Institute for Artificial Intelligence, the Cluster of Excellence for „Data-integrated simulation science“ (SimTech), and the Research Initiative „Reflecting on Intelligent Systems“ (IRIS) at the University of Stuttgart, Germany. He also holds a chair for Web and Computer Science at the University of Southampton, UK. Steffen studied in Erlangen (Germany), Philadelphia (USA) and Freiburg (Germany) computer science and computational linguistics. From 1998 to 2004, Steffen worked as a project lead and lecturer at KIT, Germany, and from 2004 to 2020 as a professor for database and information systems at the University of Koblenz, Germany. At Koblenz, he founded the Institute for Web Science and Technologies (WeST). Steffen is a fellow of the ACM, ELLIS, EurAI, and AAIA. In his research career, he has managed to avoid almost all good advice that he now gives to his team members. Such advice includes focusing on research (vs. the company) or on only one or two research areas (vs. considering ontologies, knowledge graphs, Web science, data management, machine learning, simulation, and more). Actually, improving how we understand and use text and data is a good common denominator for many of Steffen’s professional activities.
WSTNet International Seminar
STEREOTYPEMINER: Automatically Mining Stereotypical Semantic Axes in LLMs
Abstract
Stereotypes in large language models (LLMs) have been studied along a small set of predefined axes drawn from social psychology, such as warmth and competence. In this work, we introduce STEREOTYPEMINER, a framework that automatically discovers stereotypical semantic axes directly from LLM internal representations. STEREOTYPEMINER probes attention head activations to represent semantic axes as vectors, projects social group mentions onto them, and identifies stereotypical semantic axes as those along which social groups spread out significantly more than random nouns. We validate STEREOTYPEMINER against psychological surveys and demonstrate its key advantages: it outperforms layer-averaging baselines, reveals that stereotypical associations are concentrated in specific attention heads, and discovers novel stereotypical axes beyond those prescribed by social psychology theory. Our results suggest that LLMs encode a richer set of stereotypical associations than previously recognized.
Farane Jalali Farahani
Institute for Artificial Intelligence, University of Stuttgart, Germany
Speaker bio
Farane Jalali Farahani is currently pursuing her PhD studies at the Institute for Artificial Intelligence at the University of Stuttgart, Germany. She holds a background in computer science. Her research interests span natural language processing and computational social science.