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題名 促進信任的XAI：科技壓力下人工智慧解釋方法的影響分析
XAI for Trust: Analyzing the Impact of Explanation Methods under Technostress作者 鄭安佑
Zheng, An-You貢獻者 周致遠
Chou, Chih-Yuan
鄭安佑
Zheng, An-You關鍵詞 人工智慧
可解釋性
科技壓力
分心衝突理論
認知負荷
主導反應
Artificial Intelligence
Explainability
Technostress
Distraction-Conflict Theory
Cognitive Load
Dominant Response日期 2024 上傳時間 1-Feb-2024 10:56:44 (UTC+8) 摘要 隨著資通訊技術（ICTs）的持續發展，許多使用者也不經意地感受到水漲船高的壓力，我們通常稱之為「科技壓力」。這種與科技互動而產生的壓力，常以生產力下降、疲勞、與焦慮等負面效應展現。同時，人工智慧（AI）作為一種迅速發展的新興科技，一系列特殊的挑戰也應運而生。儘管在組織的決策過程中，AI的使用越來越廣泛，但AI自身決策機制並不透明，這種「黑箱」式決策降低了使用者對其的信任感，且產生的決策是否具有危險性也不得而知。有鑑於此，為了幫助AI的決策過程更加清晰，以提高使用者對其的信任，AI的可解釋性日益重要。然而，透過具可解釋性的AI（XAI）所獲得的終端使用者之信任感，是否會因為人們感受到的科技壓力而有所削弱，是一項值得討論的研究議題。因此，本研究透過分心衝突理論與認知負荷的角度，深入探討何種類別的XAI的解釋方法所帶來的信任會因為終端用戶既有的科技壓力而有所削弱。同時透過實驗與統計分析，檢測不同解釋方法對於信任的關係，以及科技壓力對該關係的調節作用。根據本研究之結果，我們發現科技壓力不會影響最終用戶對人工智慧解釋的信任，但科技壓力的存在本身與對AI的信任有顯著的負向關係。我們期許本研究可以為將來研究與實務上的應用提供一些啟發與參考。
With the continuous development of information and communication technologies (ICTs), many users inadvertently experience increasing pressure, often referred to as 'technostress'. This type of stress, arising from interactions with technology, is commonly manifested in negative effects such as decreased productivity, fatigue, and anxiety. Concurrently, artificial intelligence (AI), as a rapidly evolving technology, presents a series of unique challenges. Despite the growing use of AI in organizational decision-making processes, the opaque nature of AI decision-making mechanisms, often described as "black-box" decisions, undermines user trust. The potential risks associated with these AI-generated decisions are also uncertain. In light of this, the importance of AI explainability has grown significantly to enhance user trust in AI decision-making processes. However, whether the trust in explainable AI (XAI) among end-users is diminished due to perceived technostress is a research topic worthy of discussion. Therefore, this study delves into how certain types of XAI explanations might be less effective due to the existing technostress of end-users, exploring this through the lens of distraction-conflict theory and cognitive load. It also examines, through experiments and statistical analysis, the relationship between different explanatory methods and trust, as well as the moderating role of technostress in this relationship. Based on the results of this study, we found that technostress does not impact the end users' trust in AI explanations. However, the presence of technostress itself is significantly negatively related to trust in AI. We hope that this research can provide inspiration and references for future studies and practical applications.參考文獻 Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. S. (2018). 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國立政治大學
資訊管理學系
110356035資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110356035 資料類型 thesis dc.contributor.advisor 周致遠 zh_TW dc.contributor.advisor Chou, Chih-Yuan en_US dc.contributor.author (Authors) 鄭安佑 zh_TW dc.contributor.author (Authors) Zheng, An-You en_US dc.creator (作者) 鄭安佑 zh_TW dc.creator (作者) Zheng, An-You en_US dc.date (日期) 2024 en_US dc.date.accessioned 1-Feb-2024 10:56:44 (UTC+8) - dc.date.available 1-Feb-2024 10:56:44 (UTC+8) - dc.date.issued (上傳時間) 1-Feb-2024 10:56:44 (UTC+8) - dc.identifier (Other Identifiers) G0110356035 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/149469 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 110356035 zh_TW dc.description.abstract (摘要) 隨著資通訊技術（ICTs）的持續發展，許多使用者也不經意地感受到水漲船高的壓力，我們通常稱之為「科技壓力」。這種與科技互動而產生的壓力，常以生產力下降、疲勞、與焦慮等負面效應展現。同時，人工智慧（AI）作為一種迅速發展的新興科技，一系列特殊的挑戰也應運而生。儘管在組織的決策過程中，AI的使用越來越廣泛，但AI自身決策機制並不透明，這種「黑箱」式決策降低了使用者對其的信任感，且產生的決策是否具有危險性也不得而知。有鑑於此，為了幫助AI的決策過程更加清晰，以提高使用者對其的信任，AI的可解釋性日益重要。然而，透過具可解釋性的AI（XAI）所獲得的終端使用者之信任感，是否會因為人們感受到的科技壓力而有所削弱，是一項值得討論的研究議題。因此，本研究透過分心衝突理論與認知負荷的角度，深入探討何種類別的XAI的解釋方法所帶來的信任會因為終端用戶既有的科技壓力而有所削弱。同時透過實驗與統計分析，檢測不同解釋方法對於信任的關係，以及科技壓力對該關係的調節作用。根據本研究之結果，我們發現科技壓力不會影響最終用戶對人工智慧解釋的信任，但科技壓力的存在本身與對AI的信任有顯著的負向關係。我們期許本研究可以為將來研究與實務上的應用提供一些啟發與參考。 zh_TW dc.description.abstract (摘要) With the continuous development of information and communication technologies (ICTs), many users inadvertently experience increasing pressure, often referred to as 'technostress'. This type of stress, arising from interactions with technology, is commonly manifested in negative effects such as decreased productivity, fatigue, and anxiety. Concurrently, artificial intelligence (AI), as a rapidly evolving technology, presents a series of unique challenges. Despite the growing use of AI in organizational decision-making processes, the opaque nature of AI decision-making mechanisms, often described as "black-box" decisions, undermines user trust. The potential risks associated with these AI-generated decisions are also uncertain. In light of this, the importance of AI explainability has grown significantly to enhance user trust in AI decision-making processes. However, whether the trust in explainable AI (XAI) among end-users is diminished due to perceived technostress is a research topic worthy of discussion. Therefore, this study delves into how certain types of XAI explanations might be less effective due to the existing technostress of end-users, exploring this through the lens of distraction-conflict theory and cognitive load. It also examines, through experiments and statistical analysis, the relationship between different explanatory methods and trust, as well as the moderating role of technostress in this relationship. Based on the results of this study, we found that technostress does not impact the end users' trust in AI explanations. However, the presence of technostress itself is significantly negatively related to trust in AI. We hope that this research can provide inspiration and references for future studies and practical applications. en_US dc.description.tableofcontents CHAPTER 1. INTRODUCTION 1 CHAPTER 2. LITERATURE REVIEW 4 2.1 Technostress 4 2.2 Explainability in Artificial Intelligence 6 2.2.1 The Indispensability of Explainability 10 2.2.2 The Classifications of XAI Techniques 12 2.3 Distraction-Conflict Theory 20 2.3.1 Effects of Arousal and Cognitive Load on Dominant Responses 22 2.3.2 Cognitive Load and Working Memory 24 2.3.3 Trust in Distraction-Conflict Theory 27 CHAPTER 3. RESEARCH FRAMEWORK 30 3.1 Research Model 30 3.2 Hypothesis Development 32 3.2.1 The Effect of XAI Explanation Type on Trust 32 3.2.2 The Comparison of XAI Explanation Types on Trust 34 3.2.3 The Effect of Technostress 34 3.3 Construct Measurements 35 3.3.1 Perceived Technostress (PT) 35 3.3.2 AI Explanation Type (AIET) 37 3.3.3 Trust in AI (TAI) 39 3.3.4 Control Variables 40 CHAPTER 4. RESEARCH METHODOLOGY 41 4.1 Data Collection 41 4.2 Data Analysis 44 CHAPTER 5. RESEARCH RESULTS 46 5.1 Measurement Model Test 46 5.2 Structural Model Test 50 5.3 Model Generalization 56 CHAPTER 6. DISCUSSION 57 6.1 Interpretation of Results 57 6.2 Theoretical Contribution 60 6.3 Practical Implications 61 6.4 Limitations and Future Research 62 CHAPTER 7. CONCLUSIONS 64 REFERENCES 65 APPENDIX A 80 zh_TW dc.format.extent 1762995 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110356035 en_US dc.subject (關鍵詞) 人工智慧 zh_TW dc.subject (關鍵詞) 可解釋性 zh_TW dc.subject (關鍵詞) 科技壓力 zh_TW dc.subject (關鍵詞) 分心衝突理論 zh_TW dc.subject (關鍵詞) 認知負荷 zh_TW dc.subject (關鍵詞) 主導反應 zh_TW dc.subject (關鍵詞) Artificial Intelligence en_US dc.subject (關鍵詞) Explainability en_US dc.subject (關鍵詞) Technostress en_US dc.subject (關鍵詞) Distraction-Conflict Theory en_US dc.subject (關鍵詞) Cognitive Load en_US dc.subject (關鍵詞) Dominant Response en_US dc.title (題名) 促進信任的XAI：科技壓力下人工智慧解釋方法的影響分析 zh_TW dc.title (題名) XAI for Trust: Analyzing the Impact of Explanation Methods under Technostress en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y., & Kankanhalli, M. S. (2018). Trends and Trajectories for Explainable, Accountable and Intelligible Systems: An HCI Research Agenda. 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