In this section
@inproceedings{Sukpanichnant:2022:10.1007/978-3-031-08421-8_30,
author = {Sukpanichnant, P and Rago, A and Lertvittayakumjorn, P and Toni, F},
doi = {10.1007/978-3-031-08421-8_30},
pages = {429--444},
publisher = {Springer International Publishing},
title = {Neural QBAFs: explaining neural networks under LRP-based argumentation frameworks},
url = {http://dx.doi.org/10.1007/978-3-031-08421-8_30},
year = {2022}
}
TY - CPAPER
AB - In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.
AU - Sukpanichnant,P
AU - Rago,A
AU - Lertvittayakumjorn,P
AU - Toni,F
DO - 10.1007/978-3-031-08421-8_30
EP - 444
PB - Springer International Publishing
PY - 2022///
SN - 0302-9743
SP - 429
TI - Neural QBAFs: explaining neural networks under LRP-based argumentation frameworks
UR - http://dx.doi.org/10.1007/978-3-031-08421-8_30
UR - https://link.springer.com/book/10.1007/978-3-031-08421-8
UR - http://hdl.handle.net/10044/1/99449
ER -
