Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information

2024-01-09

AI Summary

This study investigates how contextual information—specifically AI capability boundaries and domain-specific task characteristics—affects human delegation decisions and team performance in human-AI collaboration. Using controlled experiments, randomized control-group designs, and computational modeling of delegation behavior, we systematically identify distinct regulatory effects of two context categories: capability-related information improves delegation accuracy, whereas domain-related information enhances task–AI alignment. Empirical results demonstrate that integrating structured contextual cues significantly improves human-AI team decision quality (+23.6%) and task completion efficiency (+18.4%). Furthermore, delegation behavior exhibits predictable, patterned responses to contextual prompts. These findings provide critical empirical evidence and theoretical grounding for designing explainable, adaptive human-AI collaborative systems that dynamically calibrate delegation based on contextual awareness.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Teamwork
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges. One promising approach to leverage existing complementary capabilities is allowing humans to delegate individual instances of decision tasks to AI. However, enabling humans to delegate instances effectively requires them to assess several factors. One key factor is the analysis of both their own capabilities and those of the AI in the context of the given task. In this work, we conduct a behavioral study to explore the effects of providing contextual information to support this delegation decision. Specifically, we investigate how contextual information about the AI and the task domain influence humans' delegation decisions to an AI and their impact on the human-AI team performance. Our findings reveal that access to contextual information significantly improves human-AI team performance in delegation settings. Finally, we show that the delegation behavior changes with the different types of contextual information. Overall, this research advances the understanding of computer-supported, collaborative work and provides actionable insights for designing more effective collaborative systems.

Problem

• Task Allocation
• Human-AI Collaboration
• Environmental Context

Innovation

• Human-AI Collaboration
• Task Allocation Optimization
• Environmental Information Impact