There has been explosive growth of practical AI in recent years. A major concern of current AI systems and compliance regulations is an inability to explain inferential decisions. This work explores an Explainable Artificial Intelligence (XAI) methodology that provides explanations for classification decisions. Experimental results using the MNIST handwritten digit database are provided with explainable conclusions.


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    Titel :

    Explainable Artificial Intelligence Methodology for Handwritten Applications


    Beteiligte:
    Whitten, Paul (Autor:in) / Wolff, Francis (Autor:in) / Papachristou, Chris (Autor:in)


    Erscheinungsdatum :

    16.08.2021


    Format / Umfang :

    1162137 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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