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Real-World AI Systems is an international, peer-reviewed journal dedicated to advancing research on the practical implementation, deployment, and impact of artificial intelligence (AI) technologies in real-world applications. The journal serves as a platform for researchers, engineers, policymakers, and industry practitioners to share cutting-edge findings, methodologies, and case studies that bridge the gap between theoretical AI advancements and their tangible societal, economic, and industrial benefits. The journal emphasizes robust, scalable, and ethical AI solutions that address real-world challenges across diverse domains, including healthcare, manufacturing, finance, transportation, and environmental sustainability. By fostering interdisciplinary collaboration, Real-World AI Systems aims to accelerate the transition of AI innovations from labs to real-world deployment while ensuring fairness, transparency, and accountability.
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The scope of Real-World AI Systems includes, but is not limited to, the following research areas:
- AI Deployment & Scalability
- Large-scale AI system integration
- Edge AI and distributed computing
- AI in resource-constrained environments
- Domain-Specific AI Applications
- AI in healthcare (diagnostics, treatment planning, etc.)
- AI for smart cities and infrastructure
- Industrial AI (predictive maintenance, automation, etc.)
- AI in finance and business analytics
- Ethical, Legal & Social Implications
- Fairness, bias, and accountability in AI systems
- Regulatory and policy frameworks for AI deployment
- Societal impact and public trust in AI technologies
- AI System Robustness & Reliability
- Adversarial robustness and security
- Explainable AI (XAI) and interpretability
- Testing, validation, and certification of AI systems
- Human-AI Collaboration
- Human-in-the-loop AI systems
- AI for decision support and augmentation
- User experience and interaction design for AI applications
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