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.

翻译：根据多项实证研究，尽管人机协作能够增强人类能力，但往往未能达到预期效果，且难以实现真正的协同增效。我们提出一个任务驱动框架，该框架逆转了传统方法，根据任务需求与人工智能技术能力的匹配程度来分配AI角色。通过跨风险与复杂性维度的任务分析，我们识别出三种主要AI角色：自主型、辅助/协作型与对抗型。基于当前实证发现，我们展示了如何通过将各类角色系统地映射到不同任务类型，在保持有意义的人的主体性的同时提升性能。该框架通过将任务属性与AI能力对齐，为实用有效且符合道德规范的人机协作奠定了基础，从而释放人类潜能。同时，它为情境敏感的自动化提供了结构化指导，强调补充而非取代人类判断力。