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Deep Symbolic Learning and Semantics for an Explainable and Ethical Artificial Intelligence

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Ambient Intelligence – Software and Applications (ISAmI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1239))

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Abstract

The main objective of this research is to investigate new hybrid neuro-symbolic algorithms for the construction of an open-source Deep Symbolic Learning framework that allows the training and application of explainable and ethical Deep Learning models. This framework will be supported by an ontology and a layer model in which it is taken into account which user is responsible for interpreting each of the output results according to his or her role, considering, also, the ethical implications of those results.

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Acknowledgments

This work has been partially supported by the European Regional Development Fund (ERDF) through the Spanish Ministry of Science, Innovation and University - State Research Agency under grant RTC-2017-6611-8 (TWINPICS - Social computing and sentiment analysis for detection of duplicate profiles used for terrorist propaganda and other criminal purposes).

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Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+I, Calle Espejo 2, 37007, Salamanca, Spain

    Ricardo S. Alonso

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  1. Ricardo S. Alonso
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Correspondence to Ricardo S. Alonso .

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Editors and Affiliations

  1. Departamento de Informática, University of Minho, ALGORITMI Center, Braga, Portugal

    Paulo Novais

  2. DIBRIS, University of Genoa, Genoa, Italy

    Gianni Vercelli

  3. Data Management Group, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Josep L. Larriba-Pey

  4. Department Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, Granada, Spain

    Francisco Herrera

  5. BISITE Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

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Alonso, R.S. (2021). Deep Symbolic Learning and Semantics for an Explainable and Ethical Artificial Intelligence. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_30

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