CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests

arXiv:2404.03506v1 Announce Type: cross
Abstract: Counterfactual explanations elucidate algorithmic decisions by pointing to scenarios that would have led to an alternative, desired outcome. Giving insight into the model's behavior, they hint users towards possible actions and give grounds for contesting decisions. As a crucial factor in achieving these goals, counterfactuals must be plausible, i.e., describing realistic alternative scenarios within the data manifold. This paper leverages a recently developed generative modeling technique -- adversarial random forests (ARFs) -- to efficiently generate …

abstract adversarial arxiv behavior counterfactual cs.lg decisions forests giving insight model-agnostic random random forests stat.ml type