Special Tracks

Special Track 1: Intelligent Systems under Real-World Constraints

This track focuses on intelligent systems research that explicitly addresses the challenges of real-world deployment, including resource limitations, data scarcity, noisy environments, and operational constraints.

Representative topics include:

  • Intelligent systems with limited data or low-resource settings

  • Edge AI and energy-efficient intelligent systems

  • Robust and resilient machine learning models

  • Intelligent systems operating in dynamic or uncontrolled environments

  • Scalability, reliability, and system performance trade-offs

Submissions should emphasize system behavior under constraints and provide rigorous evaluation beyond standard benchmark performance.

Special Track 2: Human–AI and Collective Intelligence Systems

This track highlights research on intelligent systems that interact with humans, organizations, or groups, emphasizing collaboration, decision support, and socio-technical integration.

Representative topics include:

  • Human-in-the-loop learning and adaptive systems

  • Collective intelligence and group decision-making systems

  • Human-centered intelligent system design

  • AI-supported organizational and policy decision processes

  • Evaluation of human–AI collaboration and interaction outcomes

Interdisciplinary studies are encouraged, provided that computational models, system architectures, or evaluation methods form a central contribution.

Special Track 3: Trustworthy and Responsible Intelligent Systems

This track addresses the growing need for intelligent systems that are transparent, fair, robust, and aligned with ethical and societal expectations in real-world use.

Representative topics include:

  • Explainable and interpretable intelligent systems in applied settings

  • Fairness, bias mitigation, and accountability in intelligent systems

  • Privacy-preserving and secure intelligent systems

  • Governance tools and frameworks for responsible AI deployment

  • System-level assessment of trustworthy AI practices

Purely normative or philosophical discussions are not within scope; submissions must include concrete system designs, methods, or empirical evaluation.