XAI and its Applications in Decision Support System

Abstract

Artificial Intelligence (AI) has transformed decision-making across various industries by enabling systems to analyse large volumes of data and provide intelligent recommendations. However, many modern AI models, particularly deep learning algorithms, function as \"black boxes,\" making it difficult for users to understand how decisions are generated. This lack of transparency limits the adoption of AI in critical domains such as healthcare, finance, law, and business. Explainable Artificial Intelligence (XAI) has emerged as a promising solution that improves transparency, interpretability, and trust in AI systems. Decision Support Systems (DSS) assist decision-makers by combining data, analytical models, and domain knowledge. Integrating XAI into DSS enables users to understand AI-generated recommendations, thereby improving confidence and accountability. This review paper explores the concepts of XAI, different explanation techniques, and their applications in DSS. The paper also discusses recent research, challenges, limitations, and future research directions.

Introduction

The study explores the integration of Explainable Artificial Intelligence (XAI) with Decision Support Systems (DSS) to improve the transparency, reliability, and trustworthiness of AI-assisted decision-making. Although Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have achieved significant success in areas such as healthcare, finance, business, and cybersecurity, many advanced models operate as black boxes, providing predictions without clearly explaining how they were generated.

Explainable Artificial Intelligence addresses this limitation by providing understandable explanations of AI decisions. It helps users identify the factors influencing predictions, evaluate the reasonableness of recommendations, detect potential biases, and improve confidence in AI systems. Decision Support Systems assist individuals and organizations by analysing data, generating recommendations, and supporting informed decisions. Integrating XAI with DSS enables users to understand both the predicted outcome and the reasoning behind it.

The literature review examines the applications of XAI in healthcare, finance, business intelligence, cybersecurity, and decision support systems. Techniques such as SHAP, LIME, Grad-CAM, and attention mechanisms help explain model predictions by identifying influential features or relevant regions in data. These methods support more transparent medical diagnoses, financial assessments, business forecasts, and cybersecurity threat detection. However, challenges remain in balancing predictive accuracy with interpretability and ensuring that explanations are understandable, efficient, and suitable for practical use.

The proposed methodology consists of six major stages:

1. Data collection: Gather relevant information from sources such as hospitals, financial institutions, business databases, and online platforms.

2. Data preprocessing: Clean the data, handle missing values, remove duplicates, and select relevant features.

3. AI model development: Apply suitable algorithms, such as Decision Trees, Random Forest, Support Vector Machines, neural networks, or deep learning models, to generate predictions.

4. Explainability layer: Use XAI techniques such as SHAP, LIME, Grad-CAM, or attention mechanisms to explain the factors influencing predictions.

5. Decision Support System: Present predictions, explanations, and recommendations through an interactive user interface.

6. Human decision-making: Allow users or domain experts to evaluate the recommendations, apply their judgment, and provide feedback for potential system improvement.

The proposed framework ensures that AI-generated recommendations are not presented as unquestionable results. Instead, decision-makers can examine the explanations, compare alternatives, and make informed final decisions based on both AI insights and their expertise.

Conclusion

Explainable Artificial Intelligence (XAI) has become an essential technology for improving the transparency and reliability of Artificial Intelligence-based Decision Support Systems. Unlike traditional black-box AI models, XAI provides clear explanations for AI-generated decisions, allowing users to understand the reasoning behind predictions. This improves user trust, supports better decision-making, and encourages the adoption of AI in critical fields such as healthcare, finance, business, education, and cybersecurity. Although significant progress has been made, several challenges still exist. Many explainability techniques are computationally expensive, and there is no universal standard for evaluating the quality of explanations. In addition, designing explanations that are both accurate and easy for non-technical users to understand remains an important research challenge. In the future, researchers are expected to develop faster, more user-friendly, and more reliable XAI techniques that can provide real-time explanations without affecting the performance of AI models. The integration of XAI with emerging technologies such as Generative AI, Large Language Models, and smart Decision Support Systems is expected to further improve transparency, fairness, and accountability. As Artificial Intelligence continues to evolve, Explainable AI will play a key role in building trustworthy and human-centred decision-making systems.

References

[1] M. T. Ribeiro, S. Singh, and C. Guestrin, \"Why Should I Trust You? Explaining the Predictions of Any Classifier,\" Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2016. [2] S. M. Lundberg and S. I. Lee, \"A Unified Approach to Interpreting Model Predictions,\" Advances in Neural Information Processing Systems (NeurIPS), 2017. [3] R. R. Selvaraju, M. Cogswell, A. Das, et al., \"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,\" Proceedings of ICCV, 2017. [4] A. B. Arrieta et al., \"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI,\" Information Fusion, vol. 58, pp. 82–115, 2020. [5] G. Vilone and L. Longo, \"Explainable Artificial Intelligence: A Systematic Review,\" Knowledge-Based Systems, 2021. [6] S. R. Islam, W. Eberle, S. K. Ghafoor, and M. Ahmed, \"Explainable Artificial Intelligence Approaches: A Survey,\" arXiv, 2021. [7] Z. C. Lipton, \"The Mythos of Model Interpretability,\" Communications of the ACM, vol. 61, no. 10, pp. 36–43, 2018. [8] F. Doshi-Velez and B. Kim, \"Towards a Rigorous Science of Interpretable Machine Learning,\" arXiv, 2017. [9] M. T. Ribeiro, S. Singh, and C. Guestrin, \"Anchors: High-Precision Model-Agnostic Explanations,\" AAAI Conference on Artificial Intelligence, 2018. [10] M. Sundararajan, A. Taly, and Q. Yan, \"Axiomatic Attribution for Deep Networks,\" Proceedings of ICML, 2017. [11] D. J. Power, Decision Support, Analytics, and Business Intelligence, Business Expert Press, 2019. [12] D. J. Power, \"Decision Support Systems: Concepts and Resources for Managers,\" Greenwood Publishing, 2002. [13] G. Kostopoulos, G. Davrazos, and S. Kotsiantis, \"Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review,\" Electronics, vol. 13, no. 14, 2024. [14] B. A. Van Dort et al., \"Explainable AI in Hospital Clinical Decision Support Systems: A Scoping Review of Healthcare Professionals\' Perspectives,\" PLOS Digital Health, 2026. [15] \"Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges,\" 2025 [16] A. Salih et al., \"A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME,\" 2023. [17] K. Gunning, \"Explainable Artificial Intelligence (XAI),\" DARPA Program, 2017. [18] W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K. Müller, Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 2019. [19] L. Floridi and J. Cowls, \"A Unified Framework of Five Principles for AI in Society,\" Harvard Data Science Review, 2019. [20] European Commission, Ethics Guidelines for Trustworthy AI, 2019. [21] OECD, OECD Principles on Artificial Intelligence, 2019. [22] J. Dorsch and M. Moll, \"Explainable and Human-Grounded AI for Decision Support Systems: The Theory of Epistemic Quasi-Partnerships,\" 2024. [23] G. Vilone and L. Longo, \"Notions of Explainability and Evaluation Approaches for Explainable Artificial Intelligence,\" Information Fusion, 2021. [24] A. Holzinger, \"From Machine Learning to Explainable AI,\" Computer, vol. 55, no. 2, 2022. [25] B. Guidotti et al., \"A Survey of Methods for Explaining Black Box Models,\" ACM Computing Surveys, vol. 51, no. 5, 2018

Copyright

Copyright © 2026 Gitin Garg, Kiya Jain, Dr. Vishal Shrivastava, Dr. Akhil Pandey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.