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International Journal Of Engineering, Business And Management(IJEBM)

Towards Resilient Intelligence: Transferable and Trustworthy AI for Real-World Systems

Srikanth Kamatala , Prudhvi Naayini


International Journal of Engineering, Business And Management(IJEBM), Vol-6,Issue-5, September - October 2022, Pages 57-62 , 10.22161/ijebm.6.5.8

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Article Info: Received: 25 Sep 2022; Received in revised form: 18 Oct 2022; Accepted: 25 Oct 2022; Available online: 30 Oct 2022

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As artificial intelligence (AI) systems become increasingly integrated into real-world applications, there is a pressing need to ensure their resilience, transferability, and trustworthiness. This paper presents a comprehensive framework for developing AI systems capable of robust performance in dynamic and uncertain environments. We explore recent advances in domain adaptation, continual learning, and explainable AI (XAI) that facilitate model generalization across domains and enhance interpretability. The study also emphasizes methods for improving trust through fairness, robustness, and verifiability of AI outputs. We examine use cases in healthcare diagnostics, autonomous systems, and predictive maintenance, highlighting the challenges of deploying AI at scale in high-stakes scenarios. Finally, we propose research directions toward resilient intelligence, including the integration of hybrid learning systems, causality-aware modeling, and zero-shot generalization. This work aims to serve as a blueprint for building AI that is not only performant, but also accountable and sustainable in complex real-world settings.

Resilience, Transferability, Trustworthiness, Explainability, Generalization

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