Abstract

In this paper, we explore the critical challenges of building trust in artificial intelligence (AI) systems, particularly those characterized by black box models. The proliferation of complex and opaque AI models has raised concerns about a lack of interpretability, hindering users� understanding and confidence in these systems Significant problem solved in this review addresses the importance of increasing the reliability of AI through semantic AI (XAI) approaches . clarify the complexity of the model To address this issue, our approach is a comprehensive review of the existing literature on XAI, black-box models, and their implications for reliability We thoroughly analyze various XAI methods, such as local interpretive model-agnostic explanations (LIME), SHapley explanatory agnostic explanations (SHAP). and reflection methods, in addition to clarifying their efforts aimed at making AI models transparent, we examine real-world case studies in which the use of XAI has enhanced trustworthiness of AI systems have improved in various sectors.

Description