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    Title: 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
    Authors: 蔡佳洵
    Tsai, Jia-Shiun
    Contributors: 陳宜秀

    Yihsiu Chen
    Po-Liang Chen

    Tsai, Jia-Shiun
    Keywords: 擬人化
    Virtual Agent
    Human-AI Interaction
    Explainable AI
    Date: 2023
    Issue Date: 2023-12-01 10:49:35 (UTC+8)
    Abstract: 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.
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