近年來，AI系統被廣泛運用於企業中，為企業增加工作效率與收益。然而，在使用AI系統時，不同的AI科技所帶來的因素會導致員工對於系統產生不信任感，進而影響到員工對於系統的使用度。另外，員工之間往往因為不同的知識背景、工作性質而產生不同差異，導致在使用系統時，不同角色間無法順利溝通。本研究旨在釐清不同角色或使用不同系統時，員工對於AI系統的認知差異並探討哪些因素會影響員工對於AI系統的信任，根據研究結果給予商業建議，以縮短不同角色間的認知差異，並增進員工對於AI系統的信任度。本研究針對半導體公司中曾經開發或使用過AI系統的員工進行問卷調查，關於員工間的認知差異部分，本研究發現開發者與使用者可以透過合作縮短認知差異，而其中未參與過系統開發的使用者，對於系統結果與過程的解釋性與開發者有極大差異，因此建議開發者應幫助未參加過系統開發的使用者釐清系統結果與過程，藉以縮短知識的差距。關於影響員工對於AI系統的信任因素方面，本研究發現（1）對於開發者而言，系統品質是相信系統的最重要因素，其中資料的品質，可能因不同使用者在資料標記上有不同看法而使資料品質不一，建議可以與不同使用者確認資料標記的正確性，或可建立標準化的方式來標記資料（2）對於大部分使用者而言，系統的使用目的在初期接觸AI時很重要，當系統目的符合員工的工作目標，使用者才有相信系統之意願。

When Artificial Intelligence (AI) systems are introduced to the industries, various issues of AI may influence the employees’ trust which causes usage intention on AI systems. However, there’s a perception gap between developers and users due to disparate expert knowledge and different perceptions of the AI systems, which hinders their communication using the same languages. This study aims to understand the perceived differences among different characters or different types of AI systems in the workplace and different factors of AI that relate to humans’ trust when using AI systems. An online survey was conducted to AI system developers and users in a semiconductor company to realize their perceptions on adopting the AI systems in their workflow. Based on the results, we first discussed the perceived differences among different characters and different system types. We found that the developers and the users could shorten their perceived differences by collaboration. The developers (D) could help the users who didn’t participate in the AI system development (U) understand how the system results been processed to shorten the knowledge gap. Then, we discussed the factors that related to AI trust. We found that (1) For the developers, the system quality would be the most important factors which related to AI trust. In addition, the data quality is not good when the data needs to be labeled. We suggest the developers (D) check the data with different users to build up a more standard labeled data. (2) For most of the users, the system purpose would be important to AI trust when they first use the AI systems. When the system purpose matches the employees’ workflow, they would have the intention to trust the AI systems. Based on the results, our research builds up trust in AI using multiple factors and bridges the perception gap between developers and users to enhance positive capability and adoptions of AI systems in the future.

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