IEEE International Workshop on Intelligent Systems for Human and Societal Wellbeing (ISHSW 2026)

Goal of the workshop 

The workshop aims to bring together researchers and practitioners working on the design and development of intelligent systems that contribute to human and societal well-being. It seeks to advance applied AI by fostering novel approaches that interpret diverse data sources, enhance system adaptability, and promote fairness, transparency, and reliability. The goal is to encourage innovations that enable AI driven systems to positively impact domains such as healthcare, assistive technology, digital forensics, smart cities, education, and environmental sustainability, ultimately improving quality of life and supporting societal progress.

Workshop theme

Recent breakthroughs in artificial intelligence, sensing, and computing have paved the way for intelligent systems that can understand, reason, a d act within complex real-world environments. As these systems are increasingly deployed in socially significant contexts, their design must balance technical performance with reliability, interpretability, and ethical considerations.

This workshop explores emerging research on applied AI and intelligent systems that deliver tangible benefits to individuals and society. It focuses on algorithms, architectures, and evaluation strategies that enable AI systems to function effectively across interdisciplinary domains from healthcare and mental health support to forensics, smart infrastructure, and education. The workshop particularly encourages contributions demonstrating how intelligent systems can operate robustly and responsibly in real-world, data driven scenarios.

Scope of the workshop

Researchers and practitioners from academia, industry, and government are invited to submit papers addressing the development, design, deployment, and evaluation of intelligent systems aimed at improving human and societal outcomes. Topics of interest include, but are not limited to:

• Applied AI and intelligent systems architectures
• Multimodal perception and fusion (vision, audio, text, physiological signals)
• Human-in-the-loop and adaptive AI systems
• AI for mental health, wellbeing, and behavioral analysis
• Computer vision and speech analysis for human wellbeing
• AI for accessibility, safety, and digital forensics
• Smart cities, mobility, and sustainable urban computing
• Ethical and responsible AI for societal applications
• Benchmarking, evaluation metrics, and datasets for applied AI systems