A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
A framework for the evaluation of XAI methods based on synthetic ground truth that relies on controlled interventions to generate synthetic datasets in which the importance of input components can be determined by design, allowing a comprehensive assessment of explanation methods in heterogeneous settings.
Miquel Miró-Nicolau, F. Spinnato, Riccardo Guidotti
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