4 citations · 6 across the 3 of their papers we have counts for
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cs.AI2023★ 2 cited
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…
cs.AI2021★ 4 cited
KANDINSKYPatterns -- An experimental exploration environment for Pattern Analysis and Machine Intelligence
Andreas Holzinger, Anna Saranti, Heimo Mueller
Machine intelligence is very successful at standard recognition tasks when having high-quality training data. There is still a significant gap between machine-level pattern recogni…