A Task-Driven Human-AI Collaboration: When to Automate, When to Collaborate, When to Challenge
For researchers and practitioners designing human-AI systems, this framework offers structured guidance for role assignment, but it is conceptual and lacks empirical validation.
The paper proposes a task-driven framework for human-AI collaboration that assigns AI roles (autonomous, assistive/collaborative, adversarial) based on task risk and complexity, aiming to improve performance while maintaining human agency. The framework is derived from empirical findings but does not report quantitative results.
According to several empirical investigations, despite enhancing human capabilities, human-AI cooperation frequently falls short of expectations and fails to reach true synergy. We propose a task-driven framework that reverses prevalent approaches by assigning AI roles according to how the task's requirements align with the capabilities of AI technology. Three major AI roles are identified through task analysis across risk and complexity dimensions: autonomous, assistive/collaborative, and adversarial. We show how proper human-AI integration maintains meaningful agency while improving performance by methodically mapping these roles to various task types based on current empirical findings. This framework lays the foundation for practically effective and morally sound human-AI collaboration that unleashes human potential by aligning task attributes to AI capabilities. It also provides structured guidance for context-sensitive automation that complements human strengths rather than replacing human judgment.