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    Title: 使用SHAP加強基於卷積神經網路的跌倒預防系統的解釋性以提高患者安全性
    Enhancing Explainability in Convolutional Neural Network-Based Fall Prevention System for Patient Safety: A SHAP-Based Approach
    Authors: 吳伊雯
    Wu, Yi-Wen
    Contributors: 張欣綠
    Chang, Hsin-Lu
    吳伊雯
    Wu, Yi-Wen
    Keywords: AI跌倒偵測系統
    可解釋性AI
    使用者信任
    使用者行為
    錯誤信號
    人機互動
    AI-based Fall Detection
    Explainable AI (XAI)
    SHapley Additive exPlanations (SHAP)
    User Trust
    User Behavior
    False Alarms
    Explainability
    Human-Machine Interaction
    Date: 2024
    Issue Date: 2024-09-04 14:03:43 (UTC+8)
    Abstract: 本研究調查了錯誤信號如何影響用戶對基於人工智能的防跌倒系統的信任和行為，重點關注錯誤報警和漏報等不準確情況對情感和行為反應的影響。這些錯誤可能會降低信任並阻礙技術的採用。我們研究了可解釋人工智能（XAI），特別是SHapley Additive exPlanations（SHAP），在提高用戶信任和系統理解方面的作用。我們的假設是，通過XAI提供清晰且全面的系統輸出解釋，能夠顯著提升用戶對該技術的態度。

    我們的研究結果揭示了錯誤率與用戶信任和滿意度之間的複雜動態。更高的錯誤率大幅降低了信任，尤其是在使用XAI的情況下。然而，錯誤率對信任的直接影響很小，其通過提高系統性能和感知可靠性所帶來的間接影響顯著增強了信任。解釋滿意度和解釋全面性模型顯示完全中介作用，表明模型對信任的影響主要是通過增強的解釋。

    這項研究強調了在促進信任方面有效錯誤管理和清晰解釋的重要性，尤其是在防跌倒等關鍵應用中。我們的研究為如何提高人工智能系統的透明度和可解釋性提供了見解，為開發者提供了寶貴的指導，以改進用戶對人工智能技術的接受度和信任度。
    This research investigates how error signals influence user trust and behavior in AI-based fall prevention systems, focusing on the emotional and behavioral responses to inaccuracies like false alarms and negatives. These errors potentially decrease trust and hinder technology adoption. We examine the role of Explainable AI (XAI), specifically SHapley Additive exPlanations (SHAP), in improving user trust and system comprehension. Our hypothesis is that clear and comprehensive explanations of system outputs via XAI significantly enhance user attitudes towards the technology.

    Our findings reveal complex dynamics between error rates and their effects on user trust and satisfaction. Higher error rates drastically reduce trust, especially when compounded by the use of XAI. However, the direct impact of error rates on trust is minimal, with their indirect effects through improved system performance and perceived reliability significantly enhancing trust. Both the Explanation Satisfaction and Comprehensiveness of Explanation models demonstrate full mediation, indicating that the impact of the model on trust is primarily through enhanced explanations.

    The study underscores the importance of effective error management and clear explanations in fostering trust, particularly in critical applications like fall prevention. Our research contributes insights on increasing transparency and explainability in AI systems, providing valuable guidance for developers to improve user receptiveness and trust in AI technologies.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    111356016
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111356016
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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