Towards Explainable Neural-Symbolic Visual Reasoning
Adrien Bennetot
and
Jean-Luc Laurent
and
Raja Chatila
and
Natalia Díaz-Rodríguez
arXiv e-Print archive - 2019 via arXiv
Keywords:
cs.LG, cs.AI
First published: 2019/09/19 (4 years ago) Abstract: Many high-performance models suffer from a lack of interpretability. There
has been an increasing influx of work on explainable artificial intelligence
(XAI) in order to disentangle what is meant and expected by XAI. Nevertheless,
there is no general consensus on how to produce and judge explanations. In this
paper, we discuss why techniques integrating connectionist and symbolic
paradigms are the most efficient solutions to produce explanations for
non-technical users and we propose a reasoning model, based on definitions by
Doran et al. [2017] (arXiv:1710.00794) to explain a neural network's decision.
We use this explanation in order to correct bias in the network's decision
rationale. We accompany this model with an example of its potential use, based
on the image captioning method in Burns et al. [2018] (arXiv:1803.09797).