Explainability and trust in AI: Bridging the gap between users and complex models

Abstract

As artificial intelligence (AI) systems continue to permeate various aspects of our daily lives, understanding and fostering trust in these complex models have become paramount. This review paper delves into the critical intersection of explainability and trust in AI, aiming to bridge the gap between users and intricate machine learning models. The evolving landscape of AI applications, ranging from predictive analytics to autonomous decision-making systems, necessitates a nuanced examination of the factors contributing to user comprehension and trust. The paper begins by elucidating the significance of explainability, delineating how transparent, interpretable models serve as the foundation for establishing trust among users. It investigates the challenges associated with increasingly intricate AI architectures, emphasizing the potential pitfalls of