政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/157856

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/157856

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政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 > Item 140.119/157856

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157856

Title:	AI賦能系統開發階段之人智協作循環 Human-AI Collaboration Cycle in the Development Stage of an AI-enabled System
Authors:	翁必揚 Weng, Pi-Yang
Contributors:	蔡瑞煌張欣綠 Tsaih, Rua-Huan Chang, Hsin-Lu 翁必揚 Weng, Pi-Yang
Keywords:	資料互動領域專家可解釋人工智慧理解度信任度 Engagement Domain expert Explainable artificial intelligence (XAI) Comprehensibility Trust
Date:	2025
Issue Date:	2025-07-01 15:32:34 (UTC+8)
Abstract:	本研究提出一個命名為人智協作循環的架構，此架構包含了資料互動、使用者理解度、使用者信任度與使用者接受度四個元素。此架構表明資料互動是人工智慧系統開發階段中，很重要的第一步，也顯示領域專家與人工智慧系統協作的必要性。我們提出四個假設：(1)使用者使用可解釋人工智慧技術的人工智慧系統，其對人工智慧系統的信任度提升，會高過於沒有使用可解釋人工智慧技術的人工智慧系統。 (2)對於有較深度認知資料互動的使用者，模型可解釋性可以讓其產生較高的使用者理解度，進而增強使用者信任。 (3) 對於有較深度認知資料互動的使用者，模型單一輸出可解釋性可以讓其產生較高的使用者理解度，進而增強使用者信任。(4) 對於有較深度認知資料互動的使用者，兼具有模型可解釋性與模型單一輸出可解釋性，與只具備一項可解釋性相比，可以讓其產生較高的使用者理解度，進而增強使用者信任。為了測試我們的假設，我們在一所醫院執行了一個實驗。實驗結果顯示：可解釋人工智慧技術是一個有用的工具，用以協助促進使用者對人工智慧系統信任度的提升，且對於有較深度資料互動的使用者，可解釋人工智慧技術確實能協助提升模型可解釋性與模型輸出理由的可解釋性，並進而增強使用者信任。本研究的結論是：資料互動是必要的，而此人智協作循環架構也將可以成為一種持續增強使用者對人工智慧賦能系統信任的方法論。 In this study, we present a framework termed as Human-AI Collaboration Cycle, encompassing four components: data engagement, user comprehensibility, user trust, and user adoption. This framework shows that data engagement is an essential first step in the AI system development stage, indicating the necessity for collaboration between domain experts and AI systems. We propose four hypotheses for this study: (1)H1: AI systems with XAI would help achieve a higher user trust enhancement than those without XAI. (2)H2: For those users with deeper data engagement, model interpretability would lead to a higher level of user comprehensibility, which in turn enhances user trust. (3)H3: For those users with deeper data engagement, instance explainability would lead to a higher level of user comprehensibility, which in turn enhances user trust. (4)H4: For those users with deeper data engagement, AI systems with both model interpretability and instance explainability would lead to a higher level of user comprehensibility, which in turn enhances user trust compared to AI systems implementing only one XAI approach in isolation. To test our hypotheses, we carry out a field experiment in a hospital. This study result shows that XAI is an useful tool to help facilitate user trust enhancement in an AI-enabled system. This study result also shows that with higher levels of data engagement, XAI does help improve model interpretation and the system output explanation, which subsequently enhances the user trust. We conclude that data engagement is essential and the human-AI collaboration cycle framework would become a methodology for continuously enhancing user trust in an AI-enabled system.
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Description:	博士國立政治大學資訊管理學系 109356507
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0109356507
Data Type:	thesis
Appears in Collections:	[Department of MIS] Theses

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