TOPML: Trading off Non-Functional Properties of Machine Learning
- Duration: 01.07.2022 - 30.06.2028
Funding Organization: Carl-Zeiss-Stiftung
Prof. Dr. Stefan Kramer
Johannes Gutenberg University Mainz
How decentralized should data be stored to protect privacy? To what extent does this affect the transparency of data and algorithms? These conflicting goals will be analyzed in a research center for machine learning.
The goal of the project is to establish an interdisciplinary research center for Machine Learning at the University of Mainz. Here, interactions and dependencies of different properties of Machine Learning will be studied. The research topics which will be investigated include transparency and fairness of data and algorithms, as well as data protection requirements and efficient use of resources such as electricity. A strong focus will be given to their competing needs. As an example, how decentralized can data be stored and processed to protect privacy? To what extent does decentralization affect the transparency of algorithms and data? What impact does this have on energy consumption? These various trade-offs will be identified and characterized to create workable trade-offs. Ethical and legal aspects will be considered.
The research project is scheduled for 6 years and is funded by the Carl Zeiss Foundation.
AI Focus Areas of the Research Project
- Machine Learning (ML): Representation Learning, (Semi) Supervised Learning, Decision Tree
- Knowledge-Based Systems
- Technology Analysis: Social and Legal Framework