A novel method for developing explainable machine learning framework using feature neutralization technique

Abstract

The rapid advancement of artificial intelligence (AI) has led to its widespread adoption across various domains. One of the most important challenges faced by AI adoption is to justify the outcome of the AI model. In response, explainable AI (XAI) has emerged as a critical area of research, aiming to enhance transparency and interpretability in AI systems. However, existing XAI methods facing several challenges, such as complexity, difficulty in interpretation, limited applicability, and lack of transparency. In this paper, we discuss current challenges using SHAP and LIME metrics being popular methods for explainable AI and then present a novel approach for developing an explainable AI framework that addresses these limitations. This novel approach uses simple techniques and understandable human explanations to provide users with clear and interpretable insights into AI model behavior. Key components of this approach include model-agnostic interpretability, a newly developed explainable factor overcoming the challenges of current XAI methods and enabling users to understand the decision-making process of AI models. We demonstrate the effectiveness of the new approach through a case study and evaluate the framework’s performance in terms of interpretability. Overall, the new approach offers enhanced transparency and trustworthiness in AI systems across diverse applications.