Learning Groupwise Explanations for Black-Box Models
Abstract
We study two user demands that are important during the exploitation of explanations in practice: 1) understanding the overall model behavior faithfully with limited cognitive load and 2) predicting the model behavior accurately on unseen instances. We illustrate that the two user demands correspond to two major sub-processes in the human cognitive process and propose a unified framework to fulfill them simultaneously. Given a local explanation method, our framework jointly 1) learns a limited number of groupwise explanations that interpret the model behavior on most instances with high fidelity and 2) specifies the region where each explanation applies. Experiments on six datasets demonstrate the effectiveness of our method.
Cite
Text
Gao et al. "Learning Groupwise Explanations for Black-Box Models." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/330Markdown
[Gao et al. "Learning Groupwise Explanations for Black-Box Models." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/gao2021ijcai-learning/) doi:10.24963/IJCAI.2021/330BibTeX
@inproceedings{gao2021ijcai-learning,
title = {{Learning Groupwise Explanations for Black-Box Models}},
author = {Gao, Jingyue and Wang, Xiting and Wang, Yasha and Yan, Yulan and Xie, Xing},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {2396-2402},
doi = {10.24963/IJCAI.2021/330},
url = {https://mlanthology.org/ijcai/2021/gao2021ijcai-learning/}
}