Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning. (arXiv:2205.03398v1 [cs.HC])

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 of explainable artificial intelligence
(XAI) has resurfaced as a topic of active research, offering approaches to
address the "how" and "why" of automated decision-making. Within this domain,
counterfactual explanations (CFEs) have gained considerable traction as a
psychologically grounded approach to generate post-hoc explanations. To do so,
CFEs highlight what changes to a model's input …

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