no code implementations • 28 Sep 2023 • Lea Demelius, Roman Kern, Andreas Trügler
Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees.
no code implementations • 7 Feb 2023 • Sebastian Scher, Bernhard Geiger, Simone Kopeinik, Andreas Trügler, Dominik Kowald
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings.
no code implementations • 17 Aug 2022 • Sebastian Scher, Simone Kopeinik, Andreas Trügler, Dominik Kowald
We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.
no code implementations • 21 Apr 2022 • Sebastian Scher, Andreas Trügler
We show both theoretically and with empirical examples that a method based on counterfactuals that was previously proposed for this is insufficient, as it is not a valid metric for determining the robustness against perturbations that occur ``naturally'', outside specific adversarial attack scenarios.