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https://ah.lib.nccu.edu.tw/handle/140.119/136850| 題名: | 機器學習可解釋技術在商業智慧中對使用者信任之影響 The Effect of Explanation on User Trust in Business Intelligence |
作者: | 侯亮宇 Hou, Liang-Yu |
貢獻者: | 林怡伶 Lin, Yi-Ling 侯亮宇 Hou, Liang-Yu |
關鍵詞: | 人機互動 機器學習 資訊視覺化 可解釋性人工智慧 信任 Human computer interaction machine learning information visualization trust explainable artificial intelligence XAI |
日期: | 2021 | 上傳時間: | 2-九月-2021 | 摘要: | 近年來機器學習引發了人工智慧 (Artificial Intelligence, AI) 應用的新趨勢。 AI 被應用於越來越複雜的任務和領域中。然而，大多數 AI 模型都在黑盒(Black box)中運行，導致人們難以理解或是分辨機器的運作以及決策過程。目前，可解 釋性人工智慧(Explainable Artificial Intelligence, XAI)，大多著重於底層演算法的 解釋，並且集中於解釋圖形識別的結果。針對終端使用者的 XAI 應用則較多專 注於支援醫療保健領域的人類決策，少有研究調查商業領域的 AI 應用程序如何 與解釋性技術相結合。本研究以商業應用上終端使用者為中心為實際業務領域中 運用 AI 技術提出了一個通用的解釋框架。該框架基於商業智慧(Business Intelligence,BI) 所開發，為終端使用者提供在機器學習不同階段的完整解釋。為 了實踐我們的框架，我們在一個航空公司行李重量預測案例上應用了這個解釋性 架構。最後，為衡量該框架實踐後的有效性，我們在 Amazon Mechanical Turk 上 進行了實驗。我們的結果表明，使用解釋性框架的參與者對模型預測更有信心， 並且更信任系統，更願意採用系統提供的建議。我們的研究使企業能夠擴展他們 的商業智能，並結合這個解釋框架的不同階段，以提高機器學習技術在商業應用 中的透明度和可靠性。 Recently, machine learning has sparked a new trend in artificial intelligence (AI) applications. AI is applied to increasingly complex tasks and in many areas. Most AI models are running in a black box resulting in difficulty for understanding. From image recognition to sentiment analysis, XAI is used to support human decision-making in the healthcare domain, yet little research has been done to investigate how AI applications in the commercial domain can be integrated with explanatory techniques. This study proposes a generalized interpretative framework for end-user-centric applications in the business domain. The framework enables the provision of complete explanations to end users at different stages based on business intelligence. To validate our framework, we applied this explanatory framework in practice using an airline baggage weight prediction case. Finally, in order to measure the effectiveness of the framework in practice, we conducted an online experiment at Mturk. Our results show that participants who use the explanatory framework have more confidence in the model predictions, trust the system, and are more willing to adopt the recommendations provided by the system. Our research allows companies to extend their business intelligence and combine different stages of this explanatory framework to improve the transparency and reliability of machine learning technology in business applications. |
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資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0108356028 | 資料類型: | thesis |
| Appears in Collections: | 學位論文 |
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