Chair of Machine Learning and Uncertainty Quantification
Welcome to the Chair of Machine Learning and Uncertainty Quantification at the Faculty of Informatics and Data Science of the University of Regensburg.
The chair is dedicated to the analysis of data as a central resource for machine learning applications and as a decisice competitive factor and innovation driver in digital markets. Research focuses on the economic effects of AI-based systems, the strategic implications of data-driven business models, the interaction between humans and AI, the explainability and acceptance of machine learning methods, empirical data protection issues and the technical and economic regulation of artificial intelligence. The measurement of uncertainty and its impact on machine learning methods are of central importance in these reserach fields.
News
Article on “Regulatory Competition in the Digital Economy” published
In his chapter “European Data Regulation: Between Data Protection and Free Flow of Data in a Global Digital Economy” of the newly published book “Regulatory Competition in the Digital Economy”, Daniel Schnurr examines the implications of European data regulation. More...
CERRE Policy Paper on the AI Act Published
The issue paper “Effective Implementation of Requirements for High-Risk AI Systems Under the AI Act” by Daniel Schnurr analyzes the AI Act’s requirements for transparency and appropriate accuracy of high-risk AI systems and provides recommendations for their technical implementation. More...
AI Act: From Act to Action - CERRE Event on February 4, 2025
On February 4, 2025, CERRE Research Fellows Pierre Larouche and Daniel Schnurr will present their research online (13:30-14:45) on the key institutional, procedural, and technical issues underlying the effective implementation of the AI Act. More information...