本研究旨在探討人工智慧代理人與人類的協作任務中，回應的語氣不確定性如何影響使用者對人工智慧的互動。近年來，隨著大型語言模型（Large Language Model,LLM）的發展，人工智慧逐漸成為具語言理解與表達能力的智慧代理人。而過去研究指出，對於科技的信任一直是影響使用者接受的重要因素，也是人機互動研究中最為關注的議題，然而，一旦人工智慧在輸出的內容中表現出偏見，則可能嚴重減弱使用者的信任。同時，相關實證研究則證實，當代理人與使用者合作的過程中表達語氣的不確定性（例如：我想…、嗯、…對吧、我覺得是這樣…）時，能向接收者傳遞對於事件的判斷並非完全篤定的態度，因此能展現如認知強迫機制（cognitive forcingfunctions）般的效果，讓使用者察覺協作過程中的資訊細節。

基於以上研究發現與觀點，本研究想進一步探討在代理人提供建議的互動情境中，不確定性的表達是否能讓使用者對潛在、可能造成危害的偏見產生察覺？同時，我們亦希望透過觀測使用者與系統之間的文字互動行為進行語氣影響的分析。為達成以上研究問題與目標，本研究設計了一項 2 組受試者組間設計（有不確定性及無不確定性）的實驗任務，請受試者扮演人資，在人工智慧代理人的建議協助下審核工作申請者。在實驗中我們在代理人的建議中刻意嵌入偏見，以檢驗使用者在不同語氣下，是否能察覺偏見的存在，並對決策行為產生影響。研究結果顯示，當人工智慧代理人以帶有不確定性的語氣提出建議時，對於偏見的察覺程度顯著高於無不確定性的建議。此外，在執行中介分析後發現，「能力感知」在偏見察覺與信任之間扮演完全中介的變項，亦即使用者在察覺到偏見後，會先降低對代理人的能力感知，從而減少對其之信任。鑑此，本研究發現不確定性可能具有認知強迫機制的特性，讓受試者不會選擇直接接受 AI 的回覆。因此，未來若在涉及人事甄選或敏感情境的 AI 應用設計中，可進一步探討適度納入不確定性語氣表達的可行性。

This study investigates how verbal uncertainty in an AI agent’s responses influences user interaction in collaborative tasks. With the rise of Large Language Models (LLMs), AI has become an intelligent agent with natural language ability. Prior research treats user trust as a key factor in technology adoption and a central topic in human-computer interaction (HCI); yet once an AI agent exhibits bias, that trust can be severely degraded. Meanwhile, studies confirm that verbal uncertainty from an agent (e.g., hedges such as “I think ...” or “..., right?”) signals that its judgment is not fully certain and can act as a cognitive forcing function, helping users notice details in the task. Building on this, the study examines whether expressing uncertainty in recommendations helps users detect latent, potentially harmful bias, and observes users’ textual interaction with the system. A 2-group between-subject experiment (uncertain vs. certain) had participants act as human resource recruiters reviewing job applicants with an AI agent’s help. A bias was embedded in the recommendations to test whether users detected it under different verbal conditions and how it affected their decisions. The results show that under uncertain phrasing, users’ perception of bias was significantly higher than under the certain condition. A mediation analysis further reveals that perceived capability fully mediates the relationship between bias perception and trust: after perceiving bias, users first lower their evaluation of the agent’s capability, which in turn reduces their trust. In sum, verbal uncertainty may function as a cognitive forcing function that keeps users from directly accepting AI outputs. These findings suggest that incorporating appropriate verbal uncertainty may be a valuable design approach for AI applications in sensitive contexts such as recruitment.

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