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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/152415


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    Title: 使用LIME建構防跌倒系統並評估對信任度的影響
    Building XAI for Fall Prevention System: An Evaluation of LIME’s Potential in Bridging Trust Gap
    Authors: 侯允禔
    Hou, Yun-Ti
    Contributors: 張欣綠
    Chang, Hsin-Lu
    侯允禔
    Hou, Yun-Ti
    Keywords: 可解釋AI
    使用者信任
    預防跌倒系統
    模型可解釋性
    XAI
    User Trust
    Fall Prevention System
    Model Interpretability
    Date: 2024
    Issue Date: 2024-08-05 12:07:45 (UTC+8)
    Abstract: 近年來，人工智慧（AI）成為相當熱門的研究課題，並迅速在醫療領域開始發展。儘管 AI 變得越來越強大且多功能，但其「黑箱」問題導致了 AI 與用戶之間的信任鴻溝。這一鴻溝可能會阻礙 AI 方法的採用，並減緩 AI 在醫療領域的發展。可解釋人工智慧（XAI）的出現為這一問題提供了潛在的解決方案，通過對模型的預測結果提供解釋，從而可能增加用戶對這些模型的信任。本研究將 XAI 演算法 LIME 和 SHAP 整合到基於 AI 的預防跌倒系統中。我們設計了一項實驗，以觀察 XAI 整合到預防跌倒系統中對三個指標的影響：用戶信任、解釋滿意度和解釋全面性，以及評估這些效果在不同準確度模型中的差異。我們的研究結果表明，提高解釋的質量和簡單性，以及在實施 XAI 前優先考慮系統準確性，對建立用戶信任至關重要。此外，以用戶為中心的設計和對解釋影響的持續評估，對於在醫療環境中有效部署 XAI 至關重要。
    In recent years, AI has become a quite popular research topic and has rapidly started to develop in the medical field. Despite AI becoming increasingly powerful and multifunctional, it has led to the problem of the 'black box', creating a trust gap between AI and its users. This gap could hinder the adoption of AI methods and decrease the development of AI in the medical sector. The emergence of XAI (Explainable AI) offers a potential solution by providing explanations for the predictive results of models, which may increase users' trust in these models. This study integrates the XAI algorithm LIME and SHAP into an AI based fall prevention system. We designed an experiment to observe how the integration of XAI into fall prevention system impacts three metrics: user trust, explanation satisfaction and explanation comprehensiveness, as well as to assess how these effects might differ across models with varying accuracy levels. Our findings indicate that enhancing the quality and simplicity of explanations, along with prioritizing system accuracy before implementing XAI, is crucial for building user trust. Furthermore, user-centric design and continuous evaluation of explanation impacts are essential for effectively deploying XAI in healthcare contexts.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    111356040
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111356040
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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