70 citations · 130 across the 41 of their papers we have counts for
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cs.CL2021★ 3 cited
More Than Words: Towards Better Quality Interpretations of Text Classifiers
Muhammad Bilal Zafar, Philipp Schmidt, Michele Donini +4
The large size and complex decision mechanisms of state-of-the-art text classifiers make it difficult for humans to understand their predictions, leading to a potential lack of tru…
cs.HC2021★ 1 cited
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated
Felix Biessmann, Dionysius Refiano
The field explainable artificial intelligence (XAI) has brought about an arsenal of methods to render Machine Learning (ML) predictions more interpretable. But how useful explanati…
cs.AI2021
A Turing Test for Transparency
Felix Biessmann, Viktor Treu
A central goal of explainable artificial intelligence (XAI) is to improve the trust relationship in human-AI interaction. One assumption underlying research in transparent AI syste…