AI4RWC: The 1st International Workshop on Artificial Intelligence for Real-world Challenges
This event is co-located with the 24th International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2025)
🗓️ 15 November, 2025
đź“Ť London (United Kingdom)
 Overview
Artificial intelligence is advancing rapidly and drawing wide attention. However, bringing AI into the real world remains a major challenge. Real-world data (RWD) typically exhibits multi-source heterogeneity, scarcity, and imbalance, usually with missingness, noise, biases, and limited labels, and constraints from privacy, compliance, and operations. These realities make reliability and maintainability central concerns. The First International Workshop on Artificial Intelligence for Real-world Challenges takes RWD as its starting point and focuses on how to build, deploy, monitor, and continually improve AI systems in real-world scenarios. We welcome diverse approaches to these challenges, including few-/zero-shot learning, data-centric AI, continual learning, etc. We also invite knowledge-driven methods, as well as retrieval-augmented generation and large language models (LLMs), and agent theory and applications that operate under real-world constraints. The workshop provides an interdisciplinary forum for researchers and practitioners worldwide to share cutting-edge research, innovative methods, and practical experience. Through keynotes, technical presentations, and panel discussions, it will foster interaction between AI researchers and domain experts, cultivate long-term collaborations, and inspire new research directions toward sustainable, impactful AI in real environments.
Scope
The workshop welcomes contributions that address interdisciplinary and real-world challenges using Artificial Intelligence. We encourage research that bridges multiple domains, integrates diverse data sources, and operates effectively under real-world scenarios. Topics of interest include, but are not limited to:
Few‑shot and zero‑shot learning for real‑world data
Transfer learning and domain adaptation in real-world scenarios
Bias detection, concept drift, data drift, and shift‑robust methods
AI Agent theory and applications
Knowledge-enhanced AI or multimodal learning for domain applications
Data-centric AI for noisy, imbalanced, and heterogeneous datasets
LLMs for real-world challenges and cross-domain collaboration
Retrieval‑augmented generation and grounding methods
Web-enabled AI systems and intelligent agents for cross-domain collaboration
AI applications in interdisciplinary fields, including healthcare, environment and earth system, materials, cultural heritage, etc.
Platforms and tools for inclusive, diversity-aware, and human-centric AI
Case studies of AI deployment and impact assessment in real-world scenarios
Sponsors
Supporting Institutions
