Intelligent Value-Co-Creation for Cross-Domain and Cross-Perspective Data Fusion in Socio-Technical Systems: Challenges and Future Research Avenues

Self-improving self-integration goes beyond the purely technical approaches pursued thus far. The aim should be to ensure that the systems provide value to the users while supporting their responsible use. This raises questions of how much autonomy intelligent systems should be granted, and to what extent human agency should be maintained in their operation. Data of high quality are a critical component at this intersection. This becomes even more important when it comes to fusing data from distinct domains, perspectives, or data that are sparse or lack sufficient annotation. In such cases, active learning, human reasoning and intuition are essential. This creates a foundation for high-quality data and for human agency and adaptive upskilling alike. In such socio-technical systems, diverse demands of various stakeholders are closely intertwined. This work proposes challenges and research avenues for a hybrid intelligence framework that foregrounds human agency and mutual learning within intelligent self-improving systems. This paper discusses the potential for learning mechanisms when it comes to the use of novel data assets. Both humans learn from intelligent systems and vice versa. Based on the general concept, we set up a research agenda and derive the next steps.

• Publication year

  2025

• Venue

  2025 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)

• Publication date

  2025-09-29

• Fields of study

  Sociology, Computer Science, Engineering

• Identifiers
  DOI 10.1109/ACSOS-C66519.2025.00040
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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