We teach across our computer science and HCI programs, and offer modules for the data science specialisation. Our lectures cover empirical and analytical methods for interdisciplinary data science; our projects cover fields like responsible AI, AI-based simulation, technology risk assessment and entrepreneurship. They pair hands-on projects with reading and discussion: students collect and analyze data about human behavior, and they build their own AI audits and studies.
We also advise students to do their own research and supervise bachelor’s and master’s theses at the intersection of AI, computational methods and social science.
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How can we use data to answer questions about people and society? This course teaches how to turn a social question into concepts and measures, then collect and analyze your own data. Topics span surveys, digital traces, text analysis, and network analysis, with applications to toxicity, democracy, and news ethics.
What does it mean to build AI systems that are fair, transparent, and accountable? In this project students learn about the principles and frameworks guiding responsible AI development. Combining hands-on research projects with weekly readings and discussions, students examine issues such as algorithmic bias, explainability, and societal impact.
How might we simulate human social behavior using AI? In this project, students build simulations with LLM-powered agents that recreate classic social science studies, from bystander effects to market panics, and test when the simulated population behaves like the real one. The groups show their simulations at the university’s Summaery exhibition, where visitors can intervene in the simulated world.
How does a product idea become a venture? In this interdisciplinary project with Media Management, student teams develop a digital product from the first idea to a working prototype: they find a problem with Design Thinking, test it in interviews with potential users, research the market, and build an MVP with a business model, presented at the university’s Summaery exhibition.
We supervise bachelor’s and master’s theses on topics at the intersection of computational methods and social science. Our linked thesis guide offers an overview of how we approach research, how we advise students, and the kinds of topics that interest us along with the steps to take if you’d like to write your thesis with us.
