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While several types of post hoc explanation methods (e.g., featureattribution methods) have been proposed in recent literature, there is littleto no work on systematically benchmarking these methods in an efficient andtransparent manner. Here, we introduce OpenXAI, a comprehensive and extensibleopen source framework for evaluating and benchmarking post hoc explanationmethods. OpenXAI comprises of the following key components: (i) a flexiblesynthetic data generator and a collection of diverse real-world datasets,pre-trained models, and state-of-the-art feature attribution methods, (ii)open-source implementations of twenty-two quantitative metrics for evaluatingfaithfulness, stability (robustness), and fairness of explanation methods, and(iii) the first ever public XAI leaderboards to benchmark explanations. OpenXAIis easily extensible, as users can readily evaluate custom explanation methodsand incorporate them into our leaderboards. Overall, OpenXAI provides anautomated end-to-end pipeline that not only simplifies and standardizes theevaluation of post hoc explanation methods, but also promotes transparency andreproducibility in benchmarking these methods. OpenXAI datasets and dataloaders, implementations of state-of-the-art explanation methods and evaluationmetrics, as well as leaderboards are publicly available athttps://open-xai.github.io/.

OpenXAI: Towards a Transparent Evaluation of Model Explanations