A Conversational Framework for Enhancing Data Explainability Using Machine Learning and Explainable Artificial Intelligence

Abstract

This paper presents a conversational artificial in-telligence (AI) framework designed to enhance data explain-ability using machine learning (ML) and explainable AI (XAI) techniques. The proposed conversational AI framework inte-grates model-agnostic explanation tools such as SHapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to help users understand how different features influence model predictions. For ML models, the pro-posed framework performs feature importance analysis and local perturbation-based explanations, allowing users to interpret and trust individual prediction outcomes. In the deep learning (DL) implementation, a feed-forward neural network (FNN) is trained on structured numerical data, with architectural components including batch normalization, dropout, and rectified linear unit (ReLU) activation to improve generalization and convergence. A multi-layer perceptron (MLP) forms the prediction engine and is tightly integrated with XAI tools to ensure transparency. The proposed conversational framework serves as an interactive interface between complex AI models and end users, enhancing interpretability and user trust. Validation results confirm that the proposed conversational explainability framework effectively enhances the interpretability of model predictions while ensuring consistent and accessible explanations across diverse datasets, thereby supporting greater transparency and accountability in AI systems. User studies further validate that the conversational interface improves users’ understanding of model behavior, sup-porting responsible AI adoption in decision-critical applications.