338 citations · 483 across the 12 of their papers we have counts for
12 papers
IMFACT: Counterfactual Explanations for Time Series via Intrinsic Mode Function Substitution
Udo Schlegel, Julian Rakuschek, Thomas Seidl +3
Oscillatory signals, such as vibration, carry class-discriminative information in specific frequency bands; perturbing them in raw feature space for counterfactual analysis easily…
When Truth Misleads -- Phase-Aware Coherence Detection for Misinformation Correction Across Epistemic Communities
Heimo Müller, Andreas Holzinger
Truth can mislead not because it is false but because delivering it through the wrong channel or authority to an audience with a different epistemic frame can harden misbelief rath…
The System Hallucination Scale (SHS): A Minimal yet Effective Human-Centered Instrument for Evaluating Hallucination-Related Behavior in Large Language Models
Heimo Müller, Dominik Steiger, Markus Plass +1
We introduce the System Hallucination Scale (SHS), a lightweight and human-centered measurement instrument for assessing hallucination-related behavior in large language models (LL…
World Models in Artificial Intelligence: Sensing, Learning, and Reasoning Like a Child
Javier Del Ser, Jesus L. Lobo, Heimo Müller +1
World Models help Artificial Intelligence (AI) predict outcomes, reason about its environment, and guide decision-making. While widely used in reinforcement learning, they lack the…
Be Careful When Evaluating Explanations Regarding Ground Truth
Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger +3
Evaluating explanations of image classifiers regarding ground truth, e.g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consid…
Explaining and visualizing black-box models through counterfactual paths
Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki +3
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a…
Ethical ChatGPT: Concerns, Challenges, and Commandments
Jianlong Zhou, Heimo Müller, Andreas Holzinger +1
Large language models, e.g. ChatGPT are currently contributing enormously to make artificial intelligence even more popular, especially among the general population. However, such…
Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization
Javier Del Ser, Alejandro Barredo-Arrieta, Natalia Díaz-Rodríguez +2
There is a broad consensus on the importance of deep learning models in tasks involving complex data. Often, an adequate understanding of these models is required when focusing on…
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications
Adrien Bennetot, Ivan Donadello, Ayoub El Qadi +10
Last years have been characterized by an upsurge of opaque automatic decision support systems, such as Deep Neural Networks (DNNs). Although they have great generalization and pred…
Graph-guided random forest for gene set selection
Bastian Pfeifer, Hubert Baniecki, Anna Saranti +2
Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific discipline…
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Julian Matschinske, Julian Späth, Reza Nasirigerdeh +29
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is o…
Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretation
André M. Carrington, Douglas G. Manuel, Paul W. Fieguth +9
Optimal performance is critical for decision-making tasks from medicine to autonomous driving, however common performance measures may be too general or too specific. For binary cl…