BibTex format
@inproceedings{Ayoobi:2023,
author = {Ayoobi, H and Potyka, N and Toni, F},
title = {SpArX: Sparse Argumentative Explanations for Neural Networks},
url = {https://www.imperial.ac.uk/people/h.ayoobi},
year = {2023}
}
RIS format (EndNote, RefMan)
TY - CPAPER
AB - Neural networks (NNs) have various applications in AI, but explaining their decisions remains challenging. Existing approaches often focus on explaining how changing individual inputs affects NNs' outputs. However, an explanation that is consistent with the input-output behaviour of an NN is not necessarily faithful to the actual mechanics thereof. In this paper, we exploit relationships between multi-layer perceptrons (MLPs) and quantitative argumentation frameworks (QAFs) to create argumentative explanations for the mechanics of MLPs. Our SpArX method first sparsifies the MLP while maintaining as much of the original structure as possible. It then translates the sparse MLP into an equivalent QAF to shed light on the underlying decision process of the MLP, producing global and/or local explanations. We demonstrate experimentally that SpArX can give more faithful explanations than existing approaches, while simultaneously providing deeper insightsinto the actual reasoning process of MLPs.
AU - Ayoobi,H
AU - Potyka,N
AU - Toni,F
PY - 2023///
TI - SpArX: Sparse Argumentative Explanations for Neural Networks
UR - https://www.imperial.ac.uk/people/h.ayoobi
ER -