Author

Johannes Fürnkranz

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Open access Sep 2026

Enhancing Rule-Based Explanations via Cognitive Bias Towards Semantic Relevance

As interest in explainable artificial intelligence (XAI) continues to grow, a critical gap remains between the explanations generated by models and their interpretability by human users, particularly when cognitive biases and semantic relevance are not adequately addressed. This paper introduces CoRIfEE-Rel, a novel hu...

Parisa Mahya, Johannes Fürnkranz · Information · 0 citations

#machine learning Preprint Sep 2026

Data Quality Rule Generation with LLMs

This paper formalizes a generalizable generate-filter framework and introduces LeDQeR, an LLM-based DQ rule generation approach that is able to produce effective and compact rule sets for various datasets and error types.

Anna-Christina Glock, Thomas Hütter, Johannes Fürnkranz et al. · 1 citation