On June 23 and 24, 2025, the second edition of “AI in the Life Sciences – An Industry Symposium” will take place at Schloss Birlinghoven in Sankt Augustin, Germany. The symposium, organized by Fraunhofer SCAI and the Bonn-Aachen International Center for Information Technology (b-it), brings together leading experts in artificial intelligence (AI) and life sciences.
Deußer, T., Leonhard, D., Hillebrand, L. P., Berger, A., Khaled, M., Heiden, S., Dilmaghani, T., Kliem, B., Loitz, R., Bauckhage, C., & Sifa, R. (2023). Uncovering Inconsistencies and Contradictions in…
The past decade of AI was largely driven by one question: how to make large language models work at all. How to scale them, stabilize them, and push their capabilities far enough to be usable.
A wonderfully diverse and international group that enriches our community year after year. As always, a major highlight: The legendary Kahoot quiz, bringing lots of fun, teamwork, and maybe even the beginning of new friendships.
A major success for the b-it: The European Research Council (ERC) is providing millions of euros for projects in the fields of computer science, economics, and evolutionary biology. Prof. Dr. Lucie Flek from Data Science & Language Technologies Group has been awarded with the popular EU funding to continue her research on social parameters in AI.
A study by the b-it proves that transformer models used in chemistry learn only statistical correlations.
In the field of Data Perspectivism, perspective has emerged as an umbrella term encompassing annotators’ points of view and culturally shaped worldviews. When modeling annotators, researchers have explored a variety of potential predictors, with demographics receiving particular attention, especially following the rise of techniques such as sociodemographic prompting. In this talk, 1 will examine the field’s strong emphasis on annotators’ sociodemographic information and highlight the limitations of this approach. I will focus on challenges in annotator modeling and the complexities of addressing highly subjective linguistic phenomena, going through data collection, modeling and evaluation.
The study of harms in NLP is a fast-evolving field of research, which in a few years has seen the need of considering the subjectivity that characterizes this
phenomenon. In this talk | present two complementary research projects that address this topic from two different perspectives. First, I discuss the systematic presence of bias against women and people with non-Western origin in data filtering strategies for harm reduction in pretraining datasets (Stranisci, & Hardmeier, C., 2025). Then, 1 describe the results of our study on canceling attitudes, whose perception appears to strongly rely on individuals’ moral stance rather than sociodemographic features (Lo, et al,
2025).
The emergence of large language models has transformed the landscape of conversational systems, but our understanding of how users interact with these systems and what they seek to accomplish remains limited. This talk presents findings from two empirical studies investigating real-world interactions with LLM-based and voice-based conversational systems. The first study analyses over 15,000 prompts submitted to Google Gemini, revealing how users formulate structured, often imperative inputs that go well beyond traditional informational,
navigational,
transactional search intents. This analysis highlights the expanding role of LLMs in supporting complex tasks such as content creation and information extraction. The second study examines over 600,000 interactions with Google Assistant across 173 users, offering insight into voice-based conversational systems’ everyday utility and limitations. The data reveal a predominance of simple instructions and a lack of deeper information-seeking behaviours. Together, these studies offer a nuanced account of user intent, interaction styles, and the evolving role of conversational systems in supporting diverse and situated information needs.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder with a lengthy prodromal phase that remains difficult to capture using traditional clinical tools. Most monitoring begins only after diagnosis, limiting insight into early symptoms and the lived experience of disease progression. In this talk, I will present work evaluating Facebook as a novel, longitudinal data source for studying PD-related disclosures across the disease timeline
-from years before diagnosis to later stages.