The Role of Explainable AI (XAI) in Trust and Adoption
Chiranjeevi Bura, Anil Kumar Jonnalagadda, Prudhvi Naayini · Journal of Artificial Intelligence General science (JAIGS) ISSN 3006-4023 · 2025
As artificial intelligence (AI) systems increasingly influence high-stakes domains such as healthcare, finance, and crim-inal justice, trust in AI decision-making becomes a crucial concern. Explainable AI (XAI) aims to enhance transparency, enabling users to understand and trust automated decisions. This paper explores the relationship between explainability and AI adoption, highlighting key methodologies in XAI, regulatory drivers, and industry-specific needs. We analyze global vs. local explainability techniques, model-specific vs. model-agnostic approaches, and the role of XAI in ensuring compliance with emerging AI regulations. Finally, we discuss challenges such as the trade-off between accuracy and interpretability and biases in explainability algorithms, offering future research directions for human-centered AI transparency.