As artificial intelligence (AI) increasingly drives decision-making in finance, ensuring transparency and trust becomes essential, particularly in high-stakes applications like portfolio optimization. This paper explores the integration of Explainable AI (XAI) interfaces within cloud-deployed portfolio optimization systems, aiming to bridge the gap between advanced AI models and financial professionals. We outline a cloud-native architecture that embeds explainability into each stage of the portfolio optimization lifecycle, from data ingestion to user interaction. Various XAI methods, including feature attribution and model-agnostic explanations, are examined in the context of financial decision-making. We present design considerations for building user-facing interfaces that make model decisions interpretable and actionable. A case study illustrates how such interfaces can enhance user trust and improve portfolio strategy validation. Finally, we discuss technical, regulatory, and usability challenges, and propose future research directions for deploying responsible AI in cloud-based financial systems.
R. Sharma, Mohammed Asif Khan· International Journal of Art...· 0 citations
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
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