政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146892

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146892

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政大典藏 > College of Commerce > Department of MIS > Theses > Item 140.119/146892

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146892

Title:	可解釋AI之類型對資訊透明度及使用者信任之影響：以假評論偵測為例 ReView: the effects of explainable AI on information transparency and user trust under fake review detection
Authors:	翁羽亮 Weng, Yu-Liang
Contributors:	簡士鎰 Chien, Shih-Yi 翁羽亮 Weng, Yu-Liang
Keywords:	XAI 使用者研究假評論偵測多重解釋類型 SAT XAI User study Fake review detection Types of explanation SAT
Date:	2023
Issue Date:	2023-09-01 14:54:24 (UTC+8)
Abstract:	自從COVID-19疫情後消費場所從實體大幅轉移至線上之後，消費者有比較多的機會參考線上評論，假評論的嚴重性也因此擴大，於是假評論偵測的議題在近年獲得許多關注，但由於AI/ML模型的黑盒特性，假評論偵測系統在現實場景中難以落地，因此本研究主要目的是發展具有解釋能力的系統，促進使用者對系統的信任程度。本研究提出三層式框架：AI/ML模型、XAI (可解釋AI)、XUI (可解釋介面)，這三個層次環環相扣，本研究基於該框架建立一個可解釋的假評論辨識系統，LSTM在系統中作為底層AI模型的算法，XAI演算法採用LIME，而在XUI上，我們操弄介面上的解釋類型與解釋層次，假評論辨識系統需要具備什麼解釋類型，或是給予多少解釋內容，是本篇研究想探討的問題。當XUI中包含全局解釋 (global explanation)、局部解釋 (local explanation)、案例解釋 (example-based explanation)三種方法時，實驗結果發現兩種解釋彼此會有互補效果，也就是說全局解釋搭配局部解或是案例解釋，會表現得比單種解釋還有效，但是當三種解釋同時出現時，局部解釋和案例解釋彼此反而會有干擾效果。此外，我們發現當三種解釋方法同時出現時，減少解釋的內容也不會影響使用者的信任程度。本篇研究除了提出可解釋系統的三層框架以外，更重要的是發現全局解釋搭配上局部解釋或案例解釋可以有效提升使用者對假評論偵測系統的信任程度。本研究發現可供線上評論平台發展假評論辨識系統，藉由本篇研究知道如何提升使用者對系統的信任程度，促進他們合理的使用假評論辨識系統。 Since the COVID-19 pandemic, consumer activities have primarily shifted from physical to online platforms, and the severity of fake reviews has increased. However, due to the black box`s nature, fake review detection systems were far from actual usage. The present study aimed to develop an explainable system to enhance user trust. This study adopted a three-layer framework: AI/ML models, eXplainable AI (XAI), and eXplainable User Interface (XUI). These three layers were interconnected, and this study built an explainable fake review detection system based on this framework. LSTM served as the AI model, LIME was the XAI algorithm, and as for the XUI layer, this study manipulated explanation types and explanation levels on the interface. When XUI included three types of explanations - global, local, and example-based- the experimental results revealed that combining global explanations with local or example-based explanations demonstrated more effectiveness than using a single type of explanation. However, when all three types of explanations appeared simultaneously, local and example-based explanations might have interfered with each other. Additionally, it was observed that when all three types of explanations were presented together, reducing the content of local explanations did not significantly impact their trust level. Besides proposing the three-layer framework for an explainable system, this research emphasized the significance of combining different types of explanations to effectively enhance user trust in the fake review detection system. Online review platforms seeking to develop fake review detection systems could benefit from this study by understanding how to improve users` trust and promote their appropriate usage of the fake review detection system.
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Description:	碩士國立政治大學資訊管理學系 110356037
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110356037
Data Type:	thesis
Appears in Collections:	[Department of MIS] Theses

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