“我没有做出微观决策”：测量、诱导和揭示协作中目标层面的人工智能贡献

摘要

随着大型语言模型（LLMs）日益深刻地影响用户形成、优化和扩展目标的方式，在人机协作中归因贡献对于用户校准自身依赖程度以及评估者评估AI辅助工作变得至关重要。然而，现有方法仅关注最终产出物，忽略了目标本身被共同塑造的过程。我们提出了一种目标级归因框架CoTrace，该框架将显性目标分解为可验证的需求，并在对话轮次中追溯直接贡献与间接影响。将CoTrace应用于638个真实世界协作日志后，我们发现：尽管模型仅占目标塑造贡献的11%-26%，但在引入更低层级的具象需求方面贡献显著更大，并且产生了多种间接贡献。通过受控模拟实验，我们证明交互设计选择会显著影响模型的目标塑造行为。在一项用户研究中，让参与者接触目标级分析后，他们对自身贡献的感知在5分量表上发生了近2分的偏移，这揭示了用户在理解自身AI辅助工作时存在系统性校准偏差。

English

As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the process through which goals themselves are jointly shaped. We introduce a goal-level attribution framework, CoTrace, that decomposes explicit goals into verifiable requirements and traces both direct contributions and indirect influences across dialogue turns. Applying CoTrace to 638 real-world collaboration logs, we find that while models account for only 11-26% of goal-shaping contribution, they contribute substantially more on introducing lower-level concrete requirements, and make various kinds of indirect contributions. Through controlled simulations, we show that interaction design choices significantly affect model goal-shaping behavior. In a user study, exposing participants to goal-level analyses shifts their perceived contributions by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand their own AI-assisted work.