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Neural Network eXplainable AI Based on Paraconsistent Analysis - an Initial Approach

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Sustainable Smart Cities and Territories (SSCTIC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 253))

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Abstract

Para-consistent logic is a non-classical logic whose foundations allow the treatment of contradictions without invalidating the conclusions. This paper presented an attempt of using Annotated Para-consistent Analysis (APA) for supporting eXplained Artificial Intelligence (XAI) on Neural-Networks. For the study case, it was presented a situation where a binary classification model was able to correctly recognize one label but not the other with the form of the principle of explosion (p\(\wedge \lnot \)p). By plotting the linear output max-min scaled data into the paraconsistent reticulate, it was possible to properly understand that the selected architectures were being able to predict. This result is early support for using APA for Neural-Network XAI, corroborating with this paper hypothesis.

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Acknowledgments

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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Authors and Affiliations

  1. Algorithm Center, University of Minho, Braga, Portugal

    Francisco S. Marcondes, Dalila Durães, Marco Gomes, Flávio Santos, José João Almeida & Paulo Novais

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  1. Francisco S. Marcondes
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  2. Dalila Durães
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  3. Marco Gomes
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  4. Flávio Santos
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  5. José João Almeida
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  6. Paulo Novais
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Corresponding author

Correspondence to Dalila Durães .

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Editors and Affiliations

  1. Department of Computing Science, Universidad Salamanca, Salamanca, Spain

    Juan M. Corchado

  2. Texas A&M University at Qatar, Doha, Qatar

    Saber Trabelsi

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Marcondes, F.S., Durães, D., Gomes, M., Santos, F., Almeida, J.J., Novais, P. (2022). Neural Network eXplainable AI Based on Paraconsistent Analysis - an Initial Approach. In: Corchado, J.M., Trabelsi, S. (eds) Sustainable Smart Cities and Territories. SSCTIC 2021. Lecture Notes in Networks and Systems, vol 253. Springer, Cham. https://doi.org/10.1007/978-3-030-78901-5_13

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