3 citations · 7 across the 11 of their papers we have counts for
4 papers · 1 filter
Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
Felix Störck, Fabian Hinder, Barbara Hammer
With the on-going integration of machine learning systems into the everyday social life of millions the notion of fairness becomes an ever increasing priority in their development.…
Causal Explanation of Concept Drift -- A Truly Actionable Approach
David Komnick, Kathrin Lammers, Barbara Hammer +2
In a world that constantly changes, it is crucial to understand how those changes impact different systems, such as industrial manufacturing or critical infrastructure. Explaining…
Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
Kathrin Lammers, Valerie Vaquet, Barbara Hammer
As machine learning is increasingly applied in an online fashion to deal with evolving data streams, the fairness of these algorithms is a matter of growing ethical and legal conce…
Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
Isaac Roberts, Alexander Schulz, Sarah Schroeder +2
Uncertainty in machine learning refers to the degree of confidence or lack thereof in a model's predictions. While uncertainty quantification methods exist, explanations of uncerta…