Summary

Our research broadly revolves around theoretical and practical aspects of machine learning with a focus on social questions. We investigate the interplay of data driven systems with society and incorporating these insights into the fundamentals of how we design and study learning systems. Such systems can range from small-scale decision-support systems, to complex industry-scale machine learning applications, recommender systems, and digital platform markets.

Specific research areas of interest include interactive learning and optimization in dynamic environments, economic incentives and strategic behavior, the role of algorithmic decision making in digital economies and labor markets, the role of AI in social science research, as well as connections to law and policy. 

Group Leader

Celestine Mendler-Dünner

• PI at the ELLIS Institute Tübingen
• Group Leader at MPI-IS
• Faculty member Tübingen AI Center
• Member of the Cluster of Excellence on ML for Science