1 code implementation • 1 May 2023 • Konstantin D. Pandl, Chun-Yin Huang, Ivan Beschastnikh, Xiaoxiao Li, Scott Thiebes, Ali Sunyaev
The valuation of data points through DDVal allows to also draw hierarchical conclusions on the contribution of institutions, and we empirically show that the accuracy of DDVal in estimating institutional contributions is higher than existing Shapley value approximation methods for federated learning.
1 code implementation • 1 May 2022 • Konstantin D. Pandl, Florian Leiser, Scott Thiebes, Ali Sunyaev
Especially bias, defined as a disparity in the model's predictive performance across different subgroups, may cause unfairness against specific subgroups, which is an undesired phenomenon for trustworthy ML models.
no code implementations • 29 Jan 2020 • Konstantin D. Pandl, Scott Thiebes, Manuel Schmidt-Kraepelin, Ali Sunyaev
Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies.