Explainable Artificial Intelligence Model: Analysis of Neural Network Parameters

Abstract

In recent years, artificial neural network is becoming a popular technology to extract the extremely complex pattern in the data across different segments of research areas and industrial applications. Most of the artificial intelligence researchers are now focused to build smart and user-friendly applications which can assist humans to make the appropriate decision in the business. The aim to build these applications is mainly to reduce the human errors and minimize influence of individual perceptions in the decision-making process. There is no doubt that this technology will be able to lead to a world where we can enjoy AI-driven applications for our day-to-day life and making some important decisions more accurately. But what if we want to know the explanation and reason behind the decision of AI system. What if we want to understand the most important factors of the decision-making processes of such applications. Due to the intense complexity of inherent structure of AI algorithm, usually researchers define the artificial neural network as “black box” whereas the traditional statistical learning models are more transparent, interpretable and explainable with respect to data and underlying business hypothesis. In this article, we will present TRAnsparent Neural Network (TRANN) by examining and explaining the network structure (model size) using statistical methods. Our aim is to create a framework to derive the right size and relevant connections of network which can explain the data and address the business queries. In this paper, we will be restricting us to analyse the feed-forward neural network model through nonlinear regression model and analyse the parameter properties guided by statistical distribution, information theoretic criteria and simulation technique.