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Post-hoc explanation methods are an important class of approaches that helpunderstand the rationale underlying a trained model's decision. But how usefulare they for an end-user towards accomplishing a given task? In this visionpaper, we argue the need for a benchmark to facilitate evaluations of theutility of post-hoc explanation methods. As a first step to this end, weenumerate desirable properties that such a benchmark should possess for thetask of debugging text classifiers. Additionally, we highlight that such abenchmark facilitates not only assessing the effectiveness of explanations butalso their efficiency.

Towards Benchmarking the Utility of Explanations for Model Debugging