OpenXAI: Towards a Transparent Evaluation of Model Explanations. (arXiv:2206.11104v1 [cs.LG])

Web: http://arxiv.org/abs/2206.11104

While several types of post hoc explanation methods (e.g., feature
attribution methods) have been proposed in recent literature, there is little
to no work on systematically benchmarking these methods in an efficient and
transparent manner. Here, we introduce OpenXAI, a comprehensive and extensible
open source framework for evaluating and benchmarking post hoc explanation
methods. OpenXAI comprises of the following key components: (i) a flexible
synthetic data generator and a collection of diverse real-world datasets,
pre-trained models, and state-of-the-art feature attribution …

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