Technology Category
Humans and AI: Human-AI Collaboration / Human-AI TeamingCognitive Modeling & Cognitive Systems: Simulating Human BehaviorMachine Learning: Large Multimodal Models (LMMs)
Architecting Human-AI Cocreation for Technical Services -- Interaction Modes and Contingency Factors
🤖 AI Summary
Large language models (LLMs) exhibit hallucination and operational fragility in technical service delivery, undermining reliability and safety. Method: This paper proposes a human–AI collaborative authoring framework tailored for technical services. Drawing inspiration from autonomous driving’s levels of autonomy, we introduce a taxonomy of six interaction paradigms—HOOTL (Human-Over-Open-Task Loop), HIC (Human-in-Control), HITP (Human-in-Task-Planning), HITL (Human-in-the-Loop), HOTL (Human-on-the-Loop), and HAM (Human-as-Monitor)—and develop a contingency-based mode selection framework grounded in task complexity, operational risk, and system reliability. Contribution/Results: Evaluated via LLM-agent experiments and multi-case studies, the framework provides technical service platforms with an actionable responsibility-allocation tool that enhances automation efficiency, safety, and contextual adaptability while preserving human oversight and control.
Technology Category
Application Category
📝 Abstract
Agentic AI systems, powered by Large Language Models (LLMs), offer transformative potential for value co-creation in technical services. However, persistent challenges like hallucinations and operational brittleness limit their autonomous use, creating a critical need for robust frameworks to guide human-AI collaboration. Drawing on established Human-AI teaming research and analogies from fields like autonomous driving, this paper develops a structured taxonomy of human-agent interaction. Based on case study research within technical support platforms, we propose a six-mode taxonomy that organizes collaboration across a spectrum of AI autonomy. This spectrum is anchored by the Human-Out-of-the-Loop (HOOTL) model for full automation and the Human-Augmented Model (HAM) for passive AI assistance. Between these poles, the framework specifies four distinct intermediate structures. These include the Human-in-Command (HIC) model, where AI proposals re-quire mandatory human approval, and the Human-in-the-Process (HITP) model for structured work-flows with deterministic human tasks. The taxonomy further delineates the Human-in-the-Loop (HITL) model, which facilitates agent-initiated escalation upon uncertainty, and the Human-on-the-Loop (HOTL) model, which enables discretionary human oversight of an autonomous AI. The primary contribution of this work is a comprehensive framework that connects this taxonomy to key contingency factors -- such as task complexity, operational risk, and system reliability -- and their corresponding conceptual architectures. By providing a systematic method for selecting and designing an appropriate level of human oversight, our framework offers practitioners a crucial tool to navigate the trade-offs between automation and control, thereby fostering the development of safer, more effective, and context-aware technical service systems.
Problem
Research questions and friction points this paper is trying to address.
Develops taxonomy for human-AI interaction in technical services
Addresses challenges like hallucinations in autonomous AI systems
Proposes framework to balance automation and human oversight
Innovation
Methods, ideas, or system contributions that make the work stand out.
Develops a six-mode human-AI interaction taxonomy
Links taxonomy to task complexity and risk factors
Provides framework for selecting human oversight levels
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