Explainable AI for Trustworthy Decision-making in IoT Environments

Authors

  • Huma Khan, Sheetal Pradip Patil, Arpit Namdev, Gitanjali Shrivastava, Nagarjuna Karyemsetty, Elangovan Muniyandy, Ankur Gupta

Keywords:

Machine learning, Blockchain, IoT, Predictive analytics

Abstract

Artificial Intelligence (AI) is being widely incorporated into Internet of Things (IoT) contexts, leading to improved automation and efficiency. Given that AI algorithms have a substantial impact on decision-making processes in these intricate ecosystems, it becomes crucial to prioritize their reliability. Explainable AI (XAI) is becoming more important for promoting openness and accountability in decision-making inside the Internet of Things (IoT). Through the provision of explanations that humans can comprehend, explainable artificial intelligence (XAI) improves stakeholders' understanding of the underlying logic and allows for the detection and reduction of any biases or mistakes. This abstract explores the importance of Explainable Artificial Intelligence (XAI) in facilitating reliable decision-making in Internet of Things (IoT) contexts. It highlights the function of XAI in improving transparency, reducing risks, and building trust among stakeholders. This abstract emphasizes the crucial need to include explainability into AI-driven decision-making processes in order to guarantee their dependability and ethical soundness. It does so by thoroughly examining XAI concepts and techniques specifically designed for the problems posed by IoT ecosystems.

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Published

26.03.2024

How to Cite

Elangovan Muniyandy, Ankur Gupta, H. K. S. P. P. A. N. G. S. N. K. . (2024). Explainable AI for Trustworthy Decision-making in IoT Environments. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1126–1135. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/5514

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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