SUNDUS: A Human-Centered Framework for Fostering Human-AI Collaboration Through Transparency

Imagine a future where a security operator can understand a complex threat in fractions of a second without specialized training. Not only that, but the operator can instantly understand the logic behind a particular AI warning. This vision of AI-augmented cognition is the focus of the proposed SUNDUS framework. However, today, this promise faces a critical barrier: the user. The immense potential of AI is often rendered useless when its operator lacks the professional training necessary to interpret and implement its outputs in the real world. This context reveals a critical research gap: while collaborative human-AI systems significantly enhance performance, their efficacy remains fundamentally dependent on extensive operator training, as evidenced by the OMAR (Operator Machine Augmentation Resource) system. The current paper proposes the SUNDUS (System for Understanding, Navigating, and Decision-Making Under Uncertainty and Support) framework, a theoretical model designed through a Human-Computer Interaction (HCI) lens to enhance human-AI collaboration by making system design a substitute for formal training. Leveraging principles from AMID (Augmented Multisensory Interface Design) and Visual Representations of Meta-Information, SUNDUS employs enhanced transparency—via Natural Language Explanations (NLEs), confidence scores, and multisensory cues—to offload cognitive burden and increase intuitive understanding. We propose a comparative experimental methodology to validate SUNDUS against OMAR, hypothesizing that SUNDUS will yield significantly higher decision-making accuracy and appropriately calibrated trust alongside a lower cognitive load in untrained users. The key implication is a scalable, human-centric design blueprint that shifts the burden of adaptation from the operator to the AI system, unlocking the full potential of augmented cognition.

@article{Hosawi2025,
  title     = {SUNDUS: A Human-Centered Framework for Fostering Human-AI Collaboration Through Transparency},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Abduljaleel Hosawi and Richard Stone},
  doi       = {10.14569/IJACSA.2025.0161104},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161104}
}