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題名 解釋的合理性及代理人擬人化程度對於虛擬代理人信任之影響
The effect of anthropomorphism and attribution plausibility on human trust in virtual agent作者 蔡佳洵
Tsai, Jia-Shiun貢獻者 陳宜秀<br>陳柏良
Yihsiu Chen<br>Po-Liang Chen
蔡佳洵
Tsai, Jia-Shiun關鍵詞 擬人化
合理性
歸因
虛擬代理人
信任
人智互動
可解釋人工智慧
Anthropomorphism
Plausibility
Attribution
Virtual Agent
Trust
Human-AI Interaction
Explainable AI日期 2023 上傳時間 1-Dec-2023 10:49:35 (UTC+8) 摘要 電腦及運算能力的快速發展使得人機互動（Human-Computer Interaction, HCI）變得越來越複雜。聊天機器人、機器人和虛擬代理等多樣化的介面形式改變了人類與系統互動的方式。早期，電腦被視為穩定且可預測的人類工具。近年來，隨著人工智慧 (AI) 的發展，電腦通常會以具有目的、動機和意圖的「代理人 (Agent) 」形式出現，而人類在協作時開始將電腦視為隊友。隊友之間的信任對於團隊建立至關重要，同樣的對於人與代理人之間的互動 (Human-Agent interaction, HAI) 也非常重要。可解釋 AI（Explainable AI, XAI）相關的研究旨在使人類用戶理解 AI 合作夥伴的行為，並提升 AI 系統的可信度 (trustworthiness) 及透明度 (transparency)。另一方面，人類也在透過電腦的外顯行為推測其隱性的內在原因。去理解和解釋所觀察到的活動的過程稱為「歸因 (attribution) 」，歸因是一種人類天生的自然能力，並在社會科學中被廣泛的研究。AI 可以展現與人類相似的歸因能力嗎？過去的研究發現，像人的特質會影響人類對於 AI 的信任。然而，像人的特質可以從很多面向來討論，例如像人的外表、像人的身體動作、像人的心理模型等。如果 AI 能夠展現隱性的人類歸因行為，人類會更信任它嗎？此外，不同的類人特質之間有什麼關聯？AI 代理人是否會因為像人的外表而被期待展現出更好的人類能力？本研究試圖透過線上的實驗流程來回答這些問題，實驗設計包含兩個因子：1. 代理外觀（像人或像機器人）和 2. AI 歸因能力（合理或不合理）。實驗設置在法律的情境之下，參與者會被告知研究團隊正在訓練一名 AI 法官進行肇事責任的判斷，參與者協助檢視 AI 法官的決策內容及對應的解釋，並衡量 AI 法官的表現及自己的信任程度。本研究結果發現，將事物擬人化的傾向使得人類期望 AI 能夠展現出像人的能力。當 AI 表現出不合理的歸因能力時，信任就會下降。此外，像人的外觀會提升人類的期望，並在期望未得到滿足時導致信任下降的更多更快。本研究結合社會心理學的概念，從人類使用者的角度切入人智互動（Human-AI interaction, HAII）研究，試圖建立人機互動和社會科學研究之間的橋樑。
Human-Computer Interaction (HCI) is becoming more complicated due to the rapid development of computing machinery. Diversified interfaces such as chatbots, robotics, and virtual agents change how humans interact with the system. Previously, humans used computers as tools that remained stable and mostly predictable. Today, with Artificial Intelligence (AI), computers often adopt the form of an 'agent' with purpose, motivation, and intentions. Humans begin to consider computers as teammates while collaborating. Trust between teammates is essential for team building, and thus also vital to Human-Agent interaction (HAI). Explainable AI (XAI) research aims to improve the trustworthiness and transparency of AI-based systems by allowing human users to understand the behavior of the AI partner. On the other hand, human is also figuring the implicit root cause out of computers' explicit behaviors. The process of understanding and interpreting observed activities is called "attribution". Attribution is a human natural ability and has been studied for a long time in social science. Can AI perform human-like attribution ability? Studies have shown that human-like qualities affect human trust in AI. However, human-like qualities can be discussed from many perspectives, for example, human-like appearance, human-like body movement, human-like mental model, etc. Will humans trust AI more if it is able to perform implicit human-like ability - - attribution? Further, what is the link between different human-like qualities? When an AI agent looks like a human, is it expected to perform the human-like abilities better? This study tried to answer these questions with an online experiment. The experiment was constructed with two variables: 1. Agent appearances (human-like or machine-like) and 2. AI attribution ability (plausible or implausible). The main setup was an AI Judge who was trained to perform responsibility allocation decisions for car accidents, and the participants were asked to review the AI Judge's performance. It was found that the tendency to anthropomorphize makes human expects AI to demonstrate human-like ability. Trust decreased when the AI demonstrated implausible attribution ability. Further, the human-form appearance increased human expectations and led to more negative trust when the expectation was not met. The study frames human-AI interaction (HAII) research from human users' perspectives by incorporating concepts of social psychology, and bridges HCI and social science research.參考文獻 Ahmad, M. (2021). Software as a medical device: Regulating ai in healthcare via responsible ai. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery Data Mining. Al-Shayea, Q. K. (2011). Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues, 8(2):150–154. 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國立政治大學
數位內容碩士學位學程
109462011資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109462011 資料類型 thesis dc.contributor.advisor 陳宜秀<br>陳柏良 zh_TW dc.contributor.advisor Yihsiu Chen<br>Po-Liang Chen en_US dc.contributor.author (Authors) 蔡佳洵 zh_TW dc.contributor.author (Authors) Tsai, Jia-Shiun en_US dc.creator (作者) 蔡佳洵 zh_TW dc.creator (作者) Tsai, Jia-Shiun en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Dec-2023 10:49:35 (UTC+8) - dc.date.available 1-Dec-2023 10:49:35 (UTC+8) - dc.date.issued (上傳時間) 1-Dec-2023 10:49:35 (UTC+8) - dc.identifier (Other Identifiers) G0109462011 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/148508 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 數位內容碩士學位學程 zh_TW dc.description (描述) 109462011 zh_TW dc.description.abstract (摘要) 電腦及運算能力的快速發展使得人機互動（Human-Computer Interaction, HCI）變得越來越複雜。聊天機器人、機器人和虛擬代理等多樣化的介面形式改變了人類與系統互動的方式。早期，電腦被視為穩定且可預測的人類工具。近年來，隨著人工智慧 (AI) 的發展，電腦通常會以具有目的、動機和意圖的「代理人 (Agent) 」形式出現，而人類在協作時開始將電腦視為隊友。隊友之間的信任對於團隊建立至關重要，同樣的對於人與代理人之間的互動 (Human-Agent interaction, HAI) 也非常重要。可解釋 AI（Explainable AI, XAI）相關的研究旨在使人類用戶理解 AI 合作夥伴的行為，並提升 AI 系統的可信度 (trustworthiness) 及透明度 (transparency)。另一方面，人類也在透過電腦的外顯行為推測其隱性的內在原因。去理解和解釋所觀察到的活動的過程稱為「歸因 (attribution) 」，歸因是一種人類天生的自然能力，並在社會科學中被廣泛的研究。AI 可以展現與人類相似的歸因能力嗎？過去的研究發現，像人的特質會影響人類對於 AI 的信任。然而，像人的特質可以從很多面向來討論，例如像人的外表、像人的身體動作、像人的心理模型等。如果 AI 能夠展現隱性的人類歸因行為，人類會更信任它嗎？此外，不同的類人特質之間有什麼關聯？AI 代理人是否會因為像人的外表而被期待展現出更好的人類能力？本研究試圖透過線上的實驗流程來回答這些問題，實驗設計包含兩個因子：1. 代理外觀（像人或像機器人）和 2. AI 歸因能力（合理或不合理）。實驗設置在法律的情境之下，參與者會被告知研究團隊正在訓練一名 AI 法官進行肇事責任的判斷，參與者協助檢視 AI 法官的決策內容及對應的解釋，並衡量 AI 法官的表現及自己的信任程度。本研究結果發現，將事物擬人化的傾向使得人類期望 AI 能夠展現出像人的能力。當 AI 表現出不合理的歸因能力時，信任就會下降。此外，像人的外觀會提升人類的期望，並在期望未得到滿足時導致信任下降的更多更快。本研究結合社會心理學的概念，從人類使用者的角度切入人智互動（Human-AI interaction, HAII）研究，試圖建立人機互動和社會科學研究之間的橋樑。 zh_TW dc.description.abstract (摘要) Human-Computer Interaction (HCI) is becoming more complicated due to the rapid development of computing machinery. Diversified interfaces such as chatbots, robotics, and virtual agents change how humans interact with the system. Previously, humans used computers as tools that remained stable and mostly predictable. Today, with Artificial Intelligence (AI), computers often adopt the form of an 'agent' with purpose, motivation, and intentions. Humans begin to consider computers as teammates while collaborating. Trust between teammates is essential for team building, and thus also vital to Human-Agent interaction (HAI). Explainable AI (XAI) research aims to improve the trustworthiness and transparency of AI-based systems by allowing human users to understand the behavior of the AI partner. On the other hand, human is also figuring the implicit root cause out of computers' explicit behaviors. The process of understanding and interpreting observed activities is called "attribution". Attribution is a human natural ability and has been studied for a long time in social science. Can AI perform human-like attribution ability? Studies have shown that human-like qualities affect human trust in AI. However, human-like qualities can be discussed from many perspectives, for example, human-like appearance, human-like body movement, human-like mental model, etc. Will humans trust AI more if it is able to perform implicit human-like ability - - attribution? Further, what is the link between different human-like qualities? When an AI agent looks like a human, is it expected to perform the human-like abilities better? This study tried to answer these questions with an online experiment. The experiment was constructed with two variables: 1. Agent appearances (human-like or machine-like) and 2. AI attribution ability (plausible or implausible). The main setup was an AI Judge who was trained to perform responsibility allocation decisions for car accidents, and the participants were asked to review the AI Judge's performance. It was found that the tendency to anthropomorphize makes human expects AI to demonstrate human-like ability. Trust decreased when the AI demonstrated implausible attribution ability. Further, the human-form appearance increased human expectations and led to more negative trust when the expectation was not met. The study frames human-AI interaction (HAII) research from human users' perspectives by incorporating concepts of social psychology, and bridges HCI and social science research. en_US dc.description.tableofcontents 1 Introduction 1 1.1 Background 1 1.2 Research Motivation 3 1.2.1 The Human Expectation on AI 3 1.2.2 The collaboration with AI 4 1.2.3 The regulation of AI 4 1.3 Research Purpose and Question 5 2 Literature Review 7 2.1 Human and Artificial Intelligence 8 2.1.1 The Development of AI 8 2.1.2 Applications of AI 9 2.1.3 Human-AI Teaming 12 2.1.4 Section Summary 13 2.2 Human Trust in AI 14 2.2.1 What is Trust? 14 2.2.2 Dynamic Trust 15 2.2.3 Human Trust and AI 16 2.2.4 Section summary 19 2.3 AI Agent and Anthropomorphism 21 2.3.1 The Application of AI agent - HAI and HRL 21 2.3.2 Anthropomorphism of AI Agent: Pros and Cons 22 2.3.3 The Multi-dimensions of Agent Anthropomorphism 24 2.3.4 Trust in human form agent 25 2.3.5 Section summary 26 2.4 Attribution 27 2.4.1 Heider’s attribution theory 27 2.4.2 Jones and Davis’s attribution theory 28 2.4.3 Weiner’s attribution theory 28 2.4.4 Kelley’s attribution theory 28 2.4.5 Causality and plausibility 29 2.4.6 Section summary 29 2.5 Natural Language Processing 31 2.5.1 The development of Natural Language Processing 31 2.5.2 Section Summary 33 2.6 Implication: Legal Technology 34 2.7 Research Questions and Hypothesis 36 3 Methodology 37 3.1 Pilot studies 38 3.1.1 To establish attribution plausibility 38 3.1.2 AI agent appearance perception 41 3.2 Main experiment 43 3.2.1 Settings 43 3.2.2 Participants 43 3.2.3 Tasks 44 3.2.4 Materials 44 3.2.5 Procudure 46 3.2.6 Measurements 49 4 Results 51 4.1 Manipulation Check 52 4.1.1 AI agent appearance 52 4.1.2 Plausibility of the explanation 52 4.2 Attitude toward AI 54 4.3 10 agent characteristic 55 4.4 The effect of agent appearance on perceived plausibility 57 4.5 Post-trial trust level 58 4.5.1 The effect of the decision discrepancy 58 4.5.2 The effect of perceived plausibility 58 4.5.3 The effect of plausibility 59 4.5.4 Comparison among sections 59 4.5.5 Comparison between section I and III 60 4.5.6 The effect of agent appearance 60 4.5.7 Interaction 62 4.5.8 The growth of trust 62 4.6 Post-section trust perception 64 4.6.1 Comparison among sections 64 4.6.2 The effect of agent appearance 65 4.6.3 Interaction 66 4.7 Post-experiment agent perception 67 4.8 Legal attitude 68 4.8.1 The effect of trust 68 4.8.2 The effect of agent appearance 68 5 Discussion 70 5.1 The characteristic of AI agent 70 5.2 Human-AI decision discrepancy 71 5.3 Plausibility of AI explanation 71 5.4 Appearance of AI agent 72 5.5 The repair of trust in AI 72 6 Conclusion 74 A Appendix: Car Accident cases 76 B Appendix: Question and Translation 78 B.1 AI attitude questions 78 B.2 10 agent characteristics 79 B.3 Post-section trust perception scale 80 B.4 Post-experiment agent perception scale 80 B.5 Legal Attitude 81 Reference 82 zh_TW dc.format.extent 2007365 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109462011 en_US dc.subject (關鍵詞) 擬人化 zh_TW dc.subject (關鍵詞) 合理性 zh_TW dc.subject (關鍵詞) 歸因 zh_TW dc.subject (關鍵詞) 虛擬代理人 zh_TW dc.subject (關鍵詞) 信任 zh_TW dc.subject (關鍵詞) 人智互動 zh_TW dc.subject (關鍵詞) 可解釋人工智慧 zh_TW dc.subject (關鍵詞) Anthropomorphism en_US dc.subject (關鍵詞) Plausibility en_US dc.subject (關鍵詞) Attribution en_US dc.subject (關鍵詞) Virtual Agent en_US dc.subject (關鍵詞) Trust en_US dc.subject (關鍵詞) Human-AI Interaction en_US dc.subject (關鍵詞) Explainable AI en_US dc.title (題名) 解釋的合理性及代理人擬人化程度對於虛擬代理人信任之影響 zh_TW dc.title (題名) The effect of anthropomorphism and attribution plausibility on human trust in virtual agent en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Ahmad, M. 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