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*AI-generated image by Google Gemini.

Workshop Aim

Human-machine collaboration leverages the complementary strengths of humans (such as reasoning, creativity, and social understanding) and AI (high computing power and big data processing capabilities) to improve problem-solving and performance of both humans and machines. The field of Human-AI Collaborative (HAIC) Systems is currently characterized by domain-specific solutions that address particular challenges. Successful applications combining humans and AI exist in healthcare, creative arts, and finance, to name a few. However, these works reveal the growing need for a systemic and generic approach to manage the complexity and heterogeneity of these systems. Indeed, effective collaboration between humans and AI requires systems that merge human and AI capabilities, adapt to each other’s needs, and integrate qualitative and quantitative metrics. Furthermore, to ensure the usability and real-world applicability of these systems, trained people working at the human-machine interface and users trained to use them are essential. At the same time, AI tools must be designed to adapt to the peculiarities, knowledge and emotional spheres of their users. This is only possible if an iterative refinement cycle is triggered, in which AI continuously learns from real-time interactions and vice versa. The development of truly adaptive collaborative systems therefore requires a redefinition of fundamental principles such as adaptability, security, trust, and explainability, while also taking into account the psychological and ethical impact on users.

The aim of the first international workshop on Human-AI Collaborative (HAIC) Systems is twofold. On the one hand, we want to provide a space for researchers and practitioners to further stimulate the expansion and deepening of each of the research directions within the HAIC field, fostering at the same time the dialogue and collaboration between the different communities. On the other hand, we want to promote a broader discussion that aims towards the definition of a general framework, capable of synthesizing common principles and driving a paradigm shift in the development of HAIC systems, thus providing a common basis for future interdisciplinary and multimodal developments.

Topics of Interest

Design and Development of HAIC Systems

  • Frameworks and methodologies for designing human-AI collaborative systems.
  • Designing principles for developing smart human-machine interfaces.
  • Principles for adaptive systems that evolve with user interactions.
  • Knowledge engineering for HAIC Systems.
  • Metrics for evaluating the quality and effectiveness of collaborations.
  • Novel approaches for the integration of human feedback into HAIC systems, in real-time.

Applications and Case Studies

  • HAIC systems in assistive robotics scenarios.
  • HAIC solutions for industrial challenges.
  • HAIC systems for precision agriculture.
  • Context-aware cobots for collaborative manufacturing.
  • Human-AI co-creation in creative arts, including but not limited to music, visual art, and poetry.
  • Collaborative decision-making systems.
  • HAIC educational systems that prioritize learner control.
  • Healthcare and precision medicine through collaborative intelligence.
  • Approaches for controlling emergent dynamics in HAIC.
  • Visionary HAIC applications in collective robotics, such as swarm robotics.
  • Applications for co-creative processes human and AI.
  • Definition of ethical guidelines for future HAIC research and developments.
  • Psychological and societal implications of HAIC technologies.