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

June 23, 2022, 1:11 a.m. | Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv evaluation lg model

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