Explainable Artificial Intelligence Methodology for Handwritten Applications

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.