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06/13/2023
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI
Counterfactual explanations (CFEs) are a popular approach in explainable...
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03/08/2023
"How to make them stay?" – Diverse Counterfactual Explanations of Employee Attrition
Employee attrition is an important and complex problem that can directly...
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12/01/2022
Explainable Artificial Intelligence for Improved Modeling of Processes
In modern business processes, the amount of data collected has increased...
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11/27/2022
"Explain it in the Same Way!" – Model-Agnostic Group Fairness of Counterfactual Explanations
Counterfactual explanations are a popular type of explanation for making...
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11/23/2022
Unsupervised Unlearning of Concept Drift with Autoencoders
The phenomena of concept drift refers to a change of the data distributi...
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11/17/2022
Spatial Graph Convolution Neural Networks for Water Distribution Systems
We investigate the task of missing value estimation in graphs as given b...
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07/05/2022
"Even if ..." – Diverse Semifactual Explanations of Reject
Machine learning based decision making systems applied in safety critica...
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06/15/2022
"Why Here and Not There?" – Diverse Contrasting Explanations of Dimensionality Reduction
Dimensionality reduction is a popular preprocessing and a widely used to...
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05/18/2022
One Explanation to Rule them All – Ensemble Consistent Explanations
Transparency is a major requirement of modern AI based decision making s...
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05/16/2022
Model Agnostic Local Explanations of Reject
The application of machine learning based decision making systems in saf...
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05/13/2022
Precise Change Point Detection using Spectral Drift Detection
The notion of concept drift refers to the phenomenon that the data gener...
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05/11/2022
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
Counterfactual explanations (CFEs) highlight what changes to a model's i...
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05/06/2022
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning
To foster usefulness and accountability of machine learning (ML), it is ...
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04/04/2022
SAM-kNN Regressor for Online Learning in Water Distribution Networks
Water distribution networks are a key component of modern infrastructure...
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02/15/2022
Explaining Reject Options of Learning Vector Quantization Classifiers
While machine learning models are usually assumed to always output a pre...
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05/17/2021
Convex optimization for actionable & plausible counterfactual explanations
Transparency is an essential requirement of machine learning based decis...
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04/06/2021
Contrastive Explanations for Explaining Model Adaptations
Many decision making systems deployed in the real world are not static -...
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03/03/2021
Fairness and Robustness of Contrasting Explanations
Fairness and explainability are two important and closely related requir...
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10/06/2020
Efficient computation of contrastive explanations
With the increasing deployment of machine learning systems in practice, ...
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02/12/2020
Convex Density Constraints for Computing Plausible Counterfactual Explanations
The increasing deployment of machine learning as well as legal regulatio...
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12/04/2019
A probability theoretic approach to drifting data in continuous time domains
The notion of drift refers to the phenomenon that the distribution, whic...
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11/15/2019
On the computation of counterfactual explanations – A survey
Due to the increasing use of machine learning in practice it becomes mor...
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08/02/2019
