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This cluster explores human–AI collaboration at work, covering interaction patterns, role definitions, and technical and organizational integration.
Defines the scope of interaction models between humans and AI, covering communication paradigms, input and output formats, error and exception handling, feedback loops, uncertainty signaling and control flows across interfaces. Excludes concrete implementation details, UI layouts, organizational processes, training pipelines, data preparation, internal model architectures and infrastructure decisions.
Concept for systematically integrating humans into automated decision and learning processes to improve quality, accountability and adaptability.
A supervisory paradigm for automated systems where humans perform oversight and escalatory interventions at a higher decision level.