Explainable Artificial Intelligence for Decision Support in Healthcare, Finance, and Engineering

Raghu Ramakrishnan

Explainable Artificial Intelligence for Decision Support in Healthcare, Finance, and Engineering

Author(s)	Raghu Ramakrishnan
Country	United States
Abstract	Explainable Artificial Intelligence (XAI) has become an essential area of research within Artificial Intelligence (AI) due to the increasing deployment of intelligent decision-support systems in high-stakes domains such as healthcare, finance, and engineering. While conventional machine learning and deep learning models often achieve high predictive accuracy, their "black-box" nature limits transparency, interpretability, accountability, and user trust. Explainable Artificial Intelligence addresses these limitations by providing human-understandable explanations for AI-generated predictions and recommendations, enabling domain experts to validate, interpret, and confidently utilize AI-assisted decisions. XAI techniques—including feature importance analysis, Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), counterfactual explanations, attention mechanisms, and inherently interpretable models—support regulatory compliance, ethical AI governance, fairness assessment, and improved human–AI collaboration. This study examines the role of Explainable Artificial Intelligence in decision support across healthcare, finance, and engineering from a multidisciplinary perspective. Employing a qualitative and analytical research methodology based on secondary data from computer science, biomedical engineering, finance, industrial engineering, information systems, and ethics literature, the study investigates XAI architectures, implementation strategies, domain-specific applications, governance frameworks, implementation challenges, and future research directions. Particular attention is given to clinical diagnosis, financial risk management, predictive maintenance, industrial automation, trustworthy AI, and responsible decision-making. The findings indicate that Explainable AI substantially improves transparency, user confidence, regulatory compliance, model validation, and decision quality while facilitating collaboration between AI systems and human experts. However, challenges related to explanation quality, computational complexity, privacy, security, fairness, and standardization remain significant. The study concludes that Explainable Artificial Intelligence is fundamental for developing trustworthy, human-centered, and ethically governed AI systems capable of supporting reliable decision-making in critical application domains.
Keywords	Explainable Artificial Intelligence, XAI, Decision Support Systems, Healthcare, Finance, Engineering, Trustworthy AI, Machine Learning.
Field	Engineering
Published In	Volume 3, Issue 4, July-August 2021
Published On	2021-07-13

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American Journal of Advanced Multidisciplinary Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Explainable Artificial Intelligence for Decision Support in Healthcare, Finance, and Engineering

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