Neural Network Explainable AI Based on Paraconsistent Analysis: An Extension
Open Access
- 30 October 2021
- journal article
- research article
- Published by MDPI AG in Electronics
- Vol. 10 (21), 2660
- https://doi.org/10.3390/electronics10212660
Abstract
This paper explores the use of paraconsistent analysis for assessing neural networks from an explainable AI perspective. This is an early exploration paper aiming to understand whether paraconsistent analysis can be applied for understanding neural networks and whether it is worth further develop the subject in future research. The answers to these two questions are affirmative. Paraconsistent analysis provides insightful prediction visualisation through a mature formal framework that provides proper support for reasoning. The significant potential envisioned is the that paraconsistent analysis will be used for guiding neural network development projects, despite the performance issues. This paper provides two explorations. The first was a baseline experiment based on MNIST for establishing the link between paraconsistency and neural networks. The second experiment aimed to detect violence in audio files to verify whether the paraconsistent framework scales to industry level problems. The conclusion shown by this early assessment is that further research on this subject is worthful, and may eventually result in a significant contribution to the field.Keywords
This publication has 12 references indexed in Scilit:
- Identifying Depression Clues using Emotions and AIPublished by INSTICC ,2021
- Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomyInformation Fusion, 2020
- Long Short-Term Memory Networks for Traffic Flow Forecasting: Exploring Input Variables, Time Frames and Multi-Step ApproachesInformatica, 2020
- Interpretable neural networks based on continuous-valued logic and multicriteria decision operatorsKnowledge-Based Systems, 2020
- Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AIInformation Fusion, 2019
- XAI—Explainable artificial intelligenceScience Robotics, 2019
- Fundus Image Classification Using VGG-19 Architecture with PCA and SVDSymmetry, 2018
- Towards Open Set Deep NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Introduction to Annotated LogicsPublished by Springer Science and Business Media LLC ,2015
- Some topological properties of paraconsistent modelsSynthese, 2013