24 citations · 57 across the 7 of their papers we have counts for
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cs.AI2022★ 24 cited
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract Setting
Ulrike Kuhl, André Artelt, Barbara Hammer
Counterfactual explanations (CFEs) highlight what changes to a model's input would have changed its prediction in a particular way. CFEs have gained considerable traction as a psyc…
cs.HC2022★ 22 cited
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning
Ulrike Kuhl, André Artelt, Barbara Hammer
To foster usefulness and accountability of machine learning (ML), it is essential to explain a model's decisions in addition to evaluating its performance. Accordingly, the field o…