44 citations · 45 across the 4 of their papers we have counts for
4 papers
ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series
Annemarie Jutte, Faizan Ahmed, Jeroen Linssen +1
This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains such as healthcare and predictive mainte…
Explainable AI to Improve Machine Learning Reliability for Industrial Cyber-Physical Systems
Annemarie Jutte, Uraz Odyurt
Industrial Cyber-Physical Systems (CPS) are sensitive infrastructure from both safety and economics perspectives, making their reliability critically important. Machine Learning (M…
C-SHAP for time series: An approach to high-level temporal explanations
Annemarie Jutte, Faizan Ahmed, Jeroen Linssen +1
In high-stakes domains, such as healthcare and industry, the explainability of AI-based decision-making has become crucial. Without insight into model reasoning, the reliability of…
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Meike Nauta, Annemarie Jutte, Jesper Provoost +1
Image recognition with prototypes is considered an interpretable alternative for black box deep learning models. Classification depends on the extent to which a test image "looks l…