AI Research
A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods
Bridging the validation gap in model transparency with synthetic ground-truth benchmarks
Evaluating explainable Artificial Intelligence (XAI) methods is a challenging task due to the lack of reliable evaluation procedures and, in particular, the absence of ground truth explanations. In the literature, existing evaluation approaches typically assess explanations by me...
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ArXiv CS.AI
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ArXiv CS.AI
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This framework addresses the critical bottleneck of validating black-box model interpretations by establishing a verifiable synthetic ground truth. It provides a standardized benchmark that allows researchers to rigorously quantify the accuracy and reliability of diverse XAI techniques.
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