American Journal of Advanced Multidisciplinary Research and Innovation

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Explainable Artificial Intelligence for Intelligent Decision Support Systems in Multidisciplinary Research and Innovation

Author(s) Yejin Choi
Country United States
Abstract The rapid advancement of Artificial Intelligence (AI) has transformed intelligent decision-making across healthcare, engineering, education, finance, agriculture, manufacturing, environmental science, public administration, and multidisciplinary research. Although advanced Machine Learning (ML) and Deep Learning (DL) models provide exceptional predictive performance, many operate as "black-box" systems, limiting transparency, accountability, and user trust. This lack of interpretability has created significant challenges in high-stakes domains where understanding the rationale behind AI-generated recommendations is essential. Explainable Artificial Intelligence (XAI) has emerged as a critical solution by providing transparent, interpretable, and human-understandable explanations that improve trust, regulatory compliance, ethical AI adoption, and collaborative decision-making.
This study presents a comprehensive analysis of Explainable Artificial Intelligence for Intelligent Decision Support Systems (IDSS) in multidisciplinary research and innovation. A qualitative analytical research methodology based on secondary data is employed to investigate explainable machine learning models, interpretable deep learning, knowledge-based systems, hybrid AI architectures, human-centred AI, and decision support frameworks. The research evaluates how XAI enhances transparency, reliability, fairness, accountability, and evidence-based decision-making across diverse research and industrial applications.
The findings indicate that Explainable AI significantly improves decision quality, user confidence, regulatory compliance, interdisciplinary collaboration, and operational efficiency by enabling stakeholders to understand model behaviour, feature importance, uncertainty, and reasoning processes. Integration with Machine Learning, Deep Learning, Natural Language Processing (NLP), Computer Vision, Knowledge Graphs, Big Data Analytics, Cloud Computing, Edge Computing, Digital Twin Technology, and Blockchain creates intelligent decision ecosystems capable of delivering transparent, secure, and context-aware recommendations. These capabilities support sustainable innovation, reproducible scientific research, and responsible AI governance.
Despite these opportunities, challenges remain concerning explanation quality, computational complexity, scalability, model accuracy–interpretability trade-offs, domain adaptation, ethical concerns, and standardisation. Future research should investigate causal explainability, multimodal XAI, federated explainable learning, quantum-enhanced explainable AI, and autonomous self-explaining intelligent systems.
The study concludes that Explainable Artificial Intelligence provides a multidisciplinary foundation for developing trustworthy, transparent, human-centred, and intelligent decision support systems capable of accelerating scientific discovery, technological innovation, and sustainable global development.
Keywords Explainable Artificial Intelligence, Intelligent Decision Support Systems, Machine Learning, Deep Learning, Transparency, Human-Centred AI, Responsible AI, Decision Intelligence, Multidisciplinary Research, Innovation.
Field Engineering
Published In Volume 2, Issue 1, January-February 2020
Published On 2020-02-29
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