「我並未做出微觀決策」：測量、誘導與揭露協作中的目標層級人工智慧貢獻

摘要

隨著大型語言模型（LLMs）日益影響使用者形成、精煉與擴展自身目標的方式，在人機協作中歸因貢獻，對於使用者校準自身的依賴程度，以及評估者評量AI輔助工作，皆變得至關重要。然而現有方法僅聚焦於最終產出成果，忽略了目標本身透過過程共同塑造的面向。我們提出一套目標層級的歸因框架——CoTrace，將明確目標拆解為可驗證的需求，並追溯對話輪次中直接的貢獻與間接的影響。將CoTrace應用於638份真實協作記錄後，我們發現：雖然模型在目標塑造貢獻中僅佔11%至26%，但在引入較低層級的具體需求方面貢獻顯著，並產生多種間接貢獻。透過控制模擬實驗，我們證明互動設計選擇會顯著影響模型的目標塑造行為。在一項使用者研究中，讓參與者接觸目標層級的分析後，其在5分量表上的感知貢獻產生近2分的偏移，顯示使用者對自身AI輔助工作的理解存在系統性失準。

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.