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

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 achie...

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Bibliographische Detailangaben

Hauptverfasser:	Dandl, Susanne, Blesch, Kristin, Freiesleben, Timo, König, Gunnar, Kapar, Jan, Bischl, Bernd, Wright, Marvin
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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