人工智能的快速进步强化了长期以来对自动化:的追求，尽可能用算法代替人类参与。这种追求隐含着这样的假设：人类之所以留在循环中，只是因为当前的人工智能系统还没有足够的能力。本文挑战了这一假设。我们不是问自动化可以扩展多远,，而是问它的概念限制在哪里，并认为即使使用高性能的人工智能系统，人类的参与也可能持续存在，原因有三个。当人类贡献人工智能无法提供的能力或观点时，技术或互补性就会出现。当参与本身对人类能动性或学习有价值时，就会出现规范或发展的基础。最重要的是, 出现理由源于目标出现: 在某些活动中, 目标没有事先完全指定，而是通过交互本身出现。在这些情况下, 人类参与不仅是提高执行力的一种手段，而且是所制定目标的组成部分。因此，人类-人工智能共建,被理解为人类和人工智能系统,共同产生结果，这不仅仅是对不完美的人工智能,的临时反应，而是通过参与而实现其目标的活动的持久特征。这种观点对于自动化的限制以及未来人工智能系统的设计,评估,和道德具有重要意义。

The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever possible. Implicit in this pursuit is the assumption that humans remain in the loop only because current AI systems are not yet sufficiently capable. This paper challenges that assumption. Rather than asking how far automation can extend, we ask where its conceptual limits lie and argue that human participation may persist even with highly capable AI systems for three distinct reasons. Technical or complementarity grounds arise when humans contribute capabilities or perspectives unavailable to AI. Normative or developmental grounds arise when participation itself is valuable for human agency or learning. Most importantly, emergence grounds arise from target emergence: in some activities, the target is not fully specified in advance but instead emerges through the interaction itself. In these cases, human participation is not merely a means of improving execution but is constitutive of the target being produced. Human--AI co-construction, understood as the joint production of outcomes by humans and AI systems, is therefore not simply a temporary response to imperfect AI, but a persistent feature of activities whose objectives emerge through participation. This perspective has important implications for the limits of automation and for the design, evaluation, and ethics of future AI systems.

科目: 人工智能 (cs.AI); 计算和语言 (cs.CL); 新兴技术 (cs.ET); 机器学习 (cs.LG); 多代理系统 (cs.MA)

Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Multiagent Systems (cs.MA)