Title: Artificial Intelligence: Theory, Tools and Real-World Applications
Editors: Prof. Dr. A. Shameem and Dr. J. Rengamani
ISBN: 978-81-69857-67-3
Chapter: 22
DOI: https://doi.org/10.59646/819/23
Author: Ms. S. Merlin Sofia
Abstract
Explainable Artificial Intelligence (XAI) has emerged as an important approach for improving transparency, accountability, and trust in AI-driven decision-making. As artificial intelligence systems are increasingly used in areas such as healthcare, finance, education, law, manufacturing, and public administration, understanding how these systems generate predictions and recommendations has become essential. Explainable AI provides techniques that help users interpret model outputs, identify influential factors, detect potential biases, and assess the reliability of automated decisions. This study examines the practical applications of XAI in supporting transparent and responsible decision-making across different domains. It highlights the role of explanation methods in improving human understanding, strengthening confidence in AI systems, facilitating regulatory compliance, and supporting human oversight. The study also considers the challenges associated with explainability, including model complexity, explanation accuracy, scalability, privacy, and the trade-off between interpretability and predictive performance. The findings indicate that effective XAI can improve decision quality by enabling stakeholders to critically evaluate AI-generated outcomes rather than relying solely on automated recommendations. Overall, the practical adoption of explainable AI can contribute to more transparent, accountable, fair, and trustworthy intelligent systems.
Keywords: Explainable Artificial Intelligence, XAI, Transparent Decision-Making, AI Interpretability, Artificial Intelligence, Trustworthy AI, Algorithmic Transparency, Accountability, Fairness, Human-AI Interaction.