Benchmarking Counterfactual Algorithms for XAI: From White Box to Black Box. (arXiv:2203.02399v2 [cs.LG] UPDATED)

This study investigates the impact of machine learning models on the
generation of counterfactual explanations by conducting a benchmark evaluation
over three different types of models: decision-tree (fully transparent,
interpretable, white-box model), a random forest (a semi-interpretable,
grey-box model), and a neural network (a fully opaque, black-box model). We
tested the counterfactual generation process using four algorithms (DiCE,
WatcherCF, prototype, and GrowingSpheresCF) in the literature in five different
datasets (COMPAS, Adult, German, Diabetes, and Breast Cancer). Our findings
indicate that: …

algorithms arxiv benchmarking black box xai