Authors:
Zeki Bilgin
1
and
Murat Gunestas
2
Affiliations:
1
Arcelik Research, Istanbul, Turkey
;
2
Cyphore Cyber Security and Forensics Initiative, Istanbul, Turkey
Keyword(s):
XAI, Explainable AI, Deep Learning, Nearest Neighbors, Neural Networks.
Abstract:
Deep Learning (DL) models exhibit dramatic success in a wide variety of fields such as human-machine interaction, computer vision, speech recognition, etc. Yet, the widespread deployment of these models partly depends on earning trust in them. Understanding how DL models reach a decision can help to build trust on these systems. In this study, we present a method for explaining inaccurate predictions of DL models through post-hoc analysis of k-nearest neighbours. More specifically, we extract k-nearest neighbours from training samples for a given mispredicted test instance, and then feed them into the model as input to observe the model’s response which is used for post-hoc analysis in comparison with the original mispredicted test sample. We apply our method on two different datasets, i.e. IRIS and CIFAR10, to show its feasibility on concrete examples.