The Dangers of Post-Hoc Interpretability: Unjustified Counterfactual Explanations
Abstract
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model. However, they create the risk of having explanations that are a result of some artifacts learned by the model instead of actual knowledge from the data. This paper focuses on the case of counterfactual explanations and asks whether the generated instances can be justified, i.e. continuously connected to some ground-truth data. We evaluate the risk of generating unjustified counterfactual examples by investigating the local neighborhoods of instances whose predictions are to be explained and show that this risk is quite high for several datasets. Furthermore, we show that most state of the art approaches do not differentiate justified from unjustified counterfactual examples, leading to less useful explanations.
Cite
Text
Laugel et al. "The Dangers of Post-Hoc Interpretability: Unjustified Counterfactual Explanations." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/388Markdown
[Laugel et al. "The Dangers of Post-Hoc Interpretability: Unjustified Counterfactual Explanations." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/laugel2019ijcai-dangers/) doi:10.24963/IJCAI.2019/388BibTeX
@inproceedings{laugel2019ijcai-dangers,
title = {{The Dangers of Post-Hoc Interpretability: Unjustified Counterfactual Explanations}},
author = {Laugel, Thibault and Lesot, Marie-Jeanne and Marsala, Christophe and Renard, Xavier and Detyniecki, Marcin},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {2801-2807},
doi = {10.24963/IJCAI.2019/388},
url = {https://mlanthology.org/ijcai/2019/laugel2019ijcai-dangers/}
}