The workshop is conducted by members of the Caius-project/ Eva Achterhold, Patrick Kaiser, Ruben Bach, Christoph Kern

Data-driven approaches for the allocation of public resources promise to make fast, reliable, cost-efficient and objective decisions. However, there are also concerns about such approaches. For example, data-driven algorithmic profiling in the context of the allocation of labor market support programs led to public outrage in Austria. Fairness concerns were raised, as gender and citizenship were found to influence allocation decisions. Thereby, they bear the risk of disparate treatment. In this workshop, we will provide an introduction to fairness notions in machine learning and discuss the possibilities and limitations of technical approaches. A data-driven profiling system for allocating support to jobseekers will be implemented in Python and provided as executable code snippets. Our aim is to discuss and evaluate fairness metrics in a realistic example.

This workshop is aimed at the participants of the summer school and will not be available online.