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arXiv · 2105.04505

Towards Benchmarking the Utility of Explanations for Model Debugging

Maximilian Idahl·Lijun Lyu·Ujwal Gadiraju·Avishek Anand
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Abstract

Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision. But how useful are they for an end-user towards accomplishing a given task? In this vision paper, we argue the need for a benchmark to facilitate evaluations of the utility of post-hoc explanation methods. As a first step to this end, we enumerate desirable properties that such a benchmark should possess for the task of debugging text classifiers. Additionally, we highlight that such a benchmark facilitates not only assessing the effectiveness of explanations but also their efficiency.

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Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju, Avishek Anand. 2021-05-10. Towards Benchmarking the Utility of Explanations for Model Debugging. https://arxiv.org/abs/2105.04505

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