Seminar Data-Science III (Social Data Science)

The achievement of the learning goals is pursued by practicing on the basis of personally assigned in-depth scientific topics as well as by actively participating in the presentation dates. The organizer will choose subject areas within the field of Data-Science (see Topics) and provide scientific papers to students to work through.

Previous participation in the courses offered by our chair are recommended.

This seminar will be split into two main topic blocks. Every student will be assigned a research paper from only one of these blocks to work on. Yet, it is expected that students also actively participate in discussion on papers from the other topic blocks after they have been presented.

When applying for this seminar, please indicate whether you would be interested in only one or both topic blocks. The two topics we are going to discuss in the HWS 2026 are:

1. Human-LLM interaction. Large language models (LLMS) are no longer just tools that retrieve information; through fluent, conversational interaction they can actively shape what people believe, decide, and do. Studies have shown that LLMs can be highly persuasive, can sway users' choices, and can influence trust. A central question is not only whether LLMs influence us, but what drives that influence: factors such as how a system is prompted, how it is trained and fine-tuned after pretraining, and behavioral tendencies like sycophancy all play a role. Because millions of people now consult these systems for advice, information, and everyday decisions, even subtle influence can accumulate into large effects across society. In this topic, we examine the mechanisms through which LLMs affect human beliefs and behavior, and how design and training choices amplify or mitigate that influence.
   • The levers of political persuasion with conversational artificial intelligence (accress through PRIMO)
   • Does Sycophancy Change Decisions? Effect of LLM Sycophancy on AI-Assisted Decision-Making
   • On the conversational persuasiveness of GPT-4
   • Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust
2. TBA

Through this seminar, students will gain a comprehensive understanding of the ethical and social dimensions of machine learning and AI, preparing them to critically engage with these technologies in their future work.

On the basis of suitable literature, in particular original scientific articles, students independently familiarize themselves with a topic in data-science, classify and narrow down the topic appropriately and develop a critical evaluation. Students work out concepts, procedures and results of a given topic clearly and with appropriate formalisms in a timely manner and to a defined extent in depth in writing; Evidence of independent development by presenting self-selected examples. Descriptive oral presentation of an in-depth data science topic using suitable media and examples in a given format.

If you are interested in this seminar, please apply to Jana Jung via email. 

Please provide a short motivation to take this seminar, a CV and your Trans­cript of Records. Also, make sure to indicate which ones of the two given topics you are interested in.