A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2023; you can also visit the original URL.
The file type is application/pdf
.
A Survey on the Robustness of Feature Importance and Counterfactual Explanations
[article]
2023
arXiv
pre-print
There exist several methods that aim to address the crucial task of understanding the behaviour of AI/ML models. Arguably, the most popular among them are local explanations that focus on investigating model behaviour for individual instances. Several methods have been proposed for local analysis, but relatively lesser effort has gone into understanding if the explanations are robust and accurately reflect the behaviour of underlying models. In this work, we present a survey of the works that
arXiv:2111.00358v2
fatcat:bvkeaw3aazbkrohtdzwxz5kijm