24 citations · 109 across the 28 of their papers we have counts for
12 papers · 1 filter
Explainable Artificial Intelligence for Improved Modeling of Processes
Riza Velioglu, Jan Philip Göpfert, André Artelt +1
In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowled…
"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations
André Artelt, Barbara Hammer
Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user…
Spatial Graph Convolution Neural Networks for Water Distribution Systems
Inaam Ashraf, Luca Hermes, André Artelt +1
We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in…
Unsupervised Unlearning of Concept Drift with Autoencoders
André Artelt, Kleanthis Malialis, Christos Panayiotou +2
Concept drift refers to a change in the data distribution affecting the data stream of future samples. Consequently, learning models operating on the data stream might become obsol…
"Even if ..." -- Diverse Semifactual Explanations of Reject
André Artelt, Barbara Hammer
Machine learning based decision making systems applied in safety critical areas require reliable high certainty predictions. For this purpose, the system can be extended by an reje…
One Explanation to Rule them All -- Ensemble Consistent Explanations
André Artelt, Stelios Vrachimis, Demetrios Eliades +2
Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A…