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Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees.
A conceptual model for leaving the data-centric approach in machine learning
For a long time, machine learning (ML) has been seen as the abstract problem of learning relationships from data independent of the surrounding settings.
Modelling the long-term fairness dynamics of data-driven targeted help on job seekers
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.
Testing robustness of predictions of trained classifiers against naturally occurring perturbations
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.
