290 citations · 881 across the 29 of their papers we have counts for
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cs.LG2020★ 22 cited
Interpretable Representations in Explainable AI: From Theory to Practice
Kacper Sokol, Peter Flach
Interpretable representations are the backbone of many explainers that target black-box predictive systems based on artificial intelligence and machine learning algorithms. They tr…
cs.LG2020★ 8 cited
LIMEtree: Consistent and Faithful Surrogate Explanations of Multiple Classes
Kacper Sokol, Peter Flach
Explainable artificial intelligence provides tools to better understand predictive models and their decisions, but many such methods are limited to producing insights with respect…
cs.LG2020★ 171 cited
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency
Kacper Sokol, Peter Flach
The need for transparency of predictive systems based on Machine Learning algorithms arises as a consequence of their ever-increasing proliferation in the industry. Whenever black-…