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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/104117完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 楊岳平 | zh_TW |
| dc.contributor.advisor | Yueh-Ping Yang | en |
| dc.contributor.author | 李裕民 | zh_TW |
| dc.contributor.author | Yu-Min Li | en |
| dc.date.accessioned | 2026-08-21T16:47:51Z | - |
| dc.date.available | 2026-08-22 | - |
| dc.date.copyright | 2026-08-21 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-08-07 11:43:53 | - |
| dc.identifier.citation | 一、中文文獻
（一）專書與專書章節（依姓氏筆畫排序） 1. 王澤鑑（2023），《侵權行為法（增補版）》。 2. 吳從周（2018），〈初探AI之民事責任-聚焦反思臺灣之實務見解〉，收於：劉靜怡主編，《人工智慧相關法律議題芻議》，頁87-116。 3. 李建良（2024），《人工智慧與法學變遷》，元照。 4. 林明鏘（2025），《德國新行政法-兼論臺灣行政法學方法論》，五南。 5. 林勤富（2023），〈人工智慧自動決策系統的透明度難題〉，收於：李建良主編，《人工智慧的法治基礎：破壞式創新的建構式法制》，第三章。 6. 楊岳平（2023），〈從人工智慧法人格到人工智慧法律實體地位-資產隔離功能的法律經濟分析觀點〉，收於：李建良主編，《人工智慧的法治基礎：破壞式創新的建構式法制》，第六章。 （二）期刊論文（依姓氏筆畫排序） 1. 王偉霖（2024），〈外送平台結合案之公平交易法疑慮-以人工智慧之運用為中心〉，《當代法律》，34期，頁5-9。 2. 余啟民（2020），平台經濟下相關消費者保護問題〉，《東吳法律學報》，32卷1期，頁41-68。 3. 宋皇志（2025），〈大型語言模型對個人資料與營業秘密保護的挑戰-風險識別與應對框架〉，《月旦法學雜誌》，365期，頁42-59。 4. 李姿瑩（2020），〈數位平臺於消費者保護法之資訊揭露義務〉，《科技法律透析》，32卷12期，頁12-17年。 5. 李建良（2022），〈法學方法 4.0：觀念與思潮〉，《台灣法律人》，10期，頁81-95。 6. 李建良（2024），〈數位主權的規範意涵：觀念論〉，《月旦法學雜誌》，355期，頁6 以下。 7. 李榮耕（2025），〈簡評詐欺犯罪危害防制條例中的資料調取規範〉，《月旦法學雜誌》，366期，頁21-31。 8. 林大洋（2013），〈民法第184條第2項之解釋與適用-從實務之觀點談「違反保護他人法律」之具體化〉，《中律會訊》，16卷3期，頁3-19。 9. 林冠亨、陳儷昕（2025），〈AI比較法研究：以外國立法例與政策選擇評析臺灣人工智慧基本法草案〉，《高雄律師會訊》，114期10-12月合刊，頁133-142。 10. 林冠亨、陳儷昕（2025），〈AI風險分級之規範設計-論歐盟與美國法之發展及對臺灣法之啟示〉，《中律會訊》，26卷3期，頁45-68。 11. 林勤富（2025），〈歐盟人工智慧法之治理侷限與制度反思〉，《中研院法學期刊》，36期，頁1-119。 12. 侯英泠（2020），〈論美食外送服務網路平台之法律性質探討-以Uber Eats為例〉，《月旦民商法雜誌》，68期，頁85-106。 13. 張凱鑫（2025），〈論人工智慧之透明度義務-從倫理原則到法律規定〉，《中正財經法學》，30期，頁127-267。 14. 張濱璿（2023），〈人工智慧演算法之法規與監理議題-自歐盟透明性要求之規範內涵談起〉，《月旦法學雜誌》，343期，頁120-135。 15. 張麗卿、周伯翰、林勤富、吳振吉、王紀軒、洪兆承、陳俊榕、韓政道、朱宸佐（2023），〈臺灣人工智慧基本法制定之必要與倡議〉，《月旦法學雜誌》，340期，頁79-101。 16. 張麗卿（2025），〈AI法規治理規範研議之比較-國際間AI法規治理之新趨勢（上）〉，《月旦法學雜誌》，359期，頁62-83。 17. 張麗卿（2025），〈我國AI基本法之研議-國際間AI法規治理之新趨勢（下）〉，《月旦法學雜誌》，360期，頁69-93。 18. 郭戎晉（2020），〈論人工智慧技術應用、法律問題定位及監管立法趨勢-以美國實務發展為核心〉，《成大法學》，39期，頁159-220。 19. 郭戎晉（2021），〈論數位時代下消費者保護立法對個人賣家之影響〉，《東海大學法學研究》，61期，頁81-133。 20. 郭戎晉（2023），〈國際趨勢下之人工智慧監管可能模式與臺灣推動課題〉，《全國律師》，27卷6期，頁18-36。 21. 郭戎晉（2023），〈人工智慧風險治理與監管機制建構之研究-以歐盟監管專法（AIA）與美國風險管理標準為核心〉，《世新法學》，17卷1號，頁109-221。 22. 郭戎晉（2023），數位平台經濟發展與消費者保護-以跨境交易爭議為核心〉，《月旦法學雜誌》，368期，頁121-144（DOI: 10.53106/1025593136806）。 23. 陳家駿（2024），〈以人為本之AI及其透明性與可解釋性-我國人工智慧基本法草案之省思與建議〉，《教育暨資訊科技法學評論》，13期，頁81-96。 24. 陳昱奉（2023），〈打擊網路詐欺之公私協力治理：敵友與虛實〉，《當代法律》，20期，頁36-43。 25. 陳昱奉（2024），〈新世代的線上治理-公私協力與跨域合作〉，《月旦律評》，30期，頁102-108（DOI: 10.53106/279069733009）。 26. 陳陽升（2023），〈從法治原則探索人工智慧之應用界限〉，《台灣法律人》，26期，頁97-106。 27. 陳陽升（2025），〈人工智慧時代與流轉的憲法規範環境：挑戰與回應〉，《憲政時代》，49卷2期，頁31-52。 28. 黃詩淳（2023），〈AI可解釋性的法學意義及其實踐〉，《國立臺灣大學法學論叢》，52卷特刊，頁931-972（DOI: 10.6199/NTULJ.202311/SP_52.0001）。 29. 劉姿汝（2010），〈網路購物契約與消費者保護〉，《科技法學評論》，7卷1期，頁201-256。 30. 楊智傑（2020），網路平台業者之責任與消費者保護之落實〉，《台灣法學雜誌》，387期，頁89-123。 31. 鄭子薇（2024），〈超商代碼繳費詐騙刑事案例分析-兼論第三方支付洗錢防詐措施〉，《月旦律評》，32期，頁66-75（DOI: 10.53106/279069733205）。 32. 蘇凱平（2025），〈以人工智慧輔佐法院之刑罰裁量：破譯「黑盒子」的可能與顧慮〉，《國立臺灣大學法學論叢》，54卷3期，頁911-925。 （三）網路資源（依姓氏筆畫排序） 1. 內政部警政署，〈165打詐儀表板〉，https://165dashboard.tw/（最後瀏覽日：2026/04/25）。 2. 蝦皮，〈帳號停權引導申訴表單〉，https://self-service.twtc.shopee.tw/appeal/616654511d22a8cfb15ba4d7（最後瀏覽日：2026/05/02）。 3. 蘇文彬，〈金融監督管理委員會揭露未來4年金融業資安韌性發展藍圖〉，《ITHOME》，https://www.ithome.com.tw/news/173100（最後瀏覽日：2026/05/10）。 （四）司法判決 1. 最高法院110年度台上字第2393號民事判決，案由：請求侵權行為損害賠償，裁判日期：民國111年6月2日。 2. 最高法院103年度台上字第1242號民事判決，案由：請求損害賠償等（返還不當得利），裁判日期：民國103年6月24日。 二、外文文獻 （一）專書與專書章節（依作者姓氏字母排序） 1. Batarseh, F. A. and Laura, J. F. (eds.) (2023), AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI, Academic Press. 2. Bradford, A. (2020), The Brussels Effect: How the European Union Rules the World, Oxford Univ. Press. 3. Hacker, P. and Jan-Hendrik Passoth (2022), “Varieties of AI Explanations under the Law: From the GDPR to the AIA, and Beyond,” in XXAI - Beyond Explainable AI, International Workshop Proceedings, 343-373, Andreas H. et al. eds.. 4. Kosseff, J. (2019), The Twenty-Six Words that Created the Internet, Cornell Univ. Press. 5. Lamberti, W. F. (2023), “An Overview of Explainable and Interpretable AI,” in AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI, 55-117, Feras A. B. and Laura J. F. eds.. 6. Michael, L. and Martin, M. (2023), Grundrechte, 8. Aufl., Nomos. 7. Pasquale, F. (2015), The Black Box Society: The Secret Algorithms that Control Money and Information, Harvard Univ. Press. 8. Shapley, L. S. (1953), “A Value for n-Person Games,” in 2 Contributions to the Theory of Games, 307-317, Harold W. K. and Albert, W. T. eds., Princeton Univ. Press. 9. Stine, A. Al-Khulaidy and Hamdi K. (2023), “Bias, Fairness, and Assurance in AI: Overview and Synthesis,” in AI Assurance: Towards Trustworthy, Explainable, Safe, and Ethical AI, 125-145, Feras A. B. and Laura, J. F. eds., Elsevier. （二）期刊文獻（依作者姓氏字母排序） 1. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M. and Kim, B. (2018), “Sanity Checks for Saliency Maps,” in 31 Advances in Neural Info. Processing Sys, pp.9505-9515. 2. Alvarez-Melis, D. and Tommi, S. J. (2018), “On the Robustness of Interpretability Methods,” in 2018 ICML Workshop on Human Interpretability in Machine Learning, pp.66-71. 3. Ananny, M. and Crawford, K. (2018), “Seeing Without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability,” New Media & Socity, 20(3), pp.973-989. 4. Arunika, M., Saranya, S., Charulekha, S., Kabilarajan, S. and Kesavan, G. (2024), “A Survey on Explainable AI Using Machine Learning Algorithms SHAP and LIME,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) pp.1-6. 5. Burrell, J. (2016), “How the Machine ‘Thinks:’ Understanding Opacity in Machine Learning Algorithms,” Big Data & Socity, 3(1), pp.1-12. 6. Citron, D. K. and Wittes, B. (2017), “The Internet Will Not Break: Denying Bad Samaritans § 230 Immunity,” Fordham Law Review, 86(2), pp.401-423. 7. Crawford, K. and Calo, R. (2016), “There Is a Blind Spot in AI Research,” Nature, Vol.538, pp.311-313. 8. Katja, de Vries (2021), “Transparent Dreams (Are Made of This): Counterfactuals as Transparency Tools in ADM,” Critical Analysis of Law, 8(1), pp.121-138. 9. Doshi-Velez Finale, and Kim, B. (2017), “Towards a Rigorous Science of Interpretable Machine Learning,” arXiv:1702.08608. 10. Doshi-Velez Finale, Kortz, M., Budish, R., Bavitz, C., Gershman, S., O’Brien, D., Scott, K., Schieber, S., Waldo, J., Weinberger, D., Weller, A. and Wood, A. (2017), “Accountability of AI under the Law: The Role of Explanation,” arXiv:1711.01134. 11. Edwards, L. and Veale, M. (2017), “Slave to the Algorithm? Why a ‘Right to an Explanation’ Is Probably Not the Remedy You Are Looking For,” Duke Law & Technology Review, 16(1), pp.18-84. 12. Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S. and Su-In Lee, “Improving Performance of Deep Learning Models with Axiomatic Attribution Priors and Expected Gradients,” Nature Machine Intelligence, Vol.3, oo.620-631. 13. Goldman, E. (2019), “Why Section 230 is Better than the First Amendment,” Notre Dame Law Review Reflection, 95(1), pp.33-45. 14. Grochowski, M., Jabłonowska, A., Lagioia, F. amd Sartor, G. (2021) “Algorithmic Transparency and Explainability for EU Consumer Protection: Unwrapping the Regulatory Premises,” Critical Analysis of Law, 8(1), pp.43-63. 15. Hacker, P. (2023), “The European AI Liability Directives-Critique of a Half-Hearted Approach and Lessons for the Future,” Computer Law and Security, Vol.51. 16. Nguyen, Hoang-Minh, Hoang, Trong-Minh and Thi Trang Linh Le (2024), “Comparing XAI Methods for Identifying Critical Features and Expert Contributions in Multi-Domain Datasets,” in Proceedings of the 2024 IEEE International Conference on Big Data, pp.5876-5885. 17. Holmes, O. W. (1899), “The Theory of Legal Interpretation,” Harvard Law Review, 12(6), pp.417-420. 18. Sarthak, J. and Wallace, B. C. (2019), “Attention is not Explanation,” in Proceedings of NAACL-HLT 2019, Vol.1, pp.3543-3556. 19. Kaminski, M. E. (2019), “The Right to Explanation, Explained,” Berkeley Technology Law Journal, 34(1), pp.189-218. 20. Keller, D. (2023), “Platform Transparency and the First Amendment,” Journal of Free Speech Law, Vol.3, pp.1-86. 21. Koivisto, I. (2021), “Transparency in the Digital Environment,” Critical Analysis of Law, 8(1), pp.1-8. 22. Lazcoz, G. (2023), “Automated Decision-Making under Amsterdam’s District Court Judgments: Drivers v. Uber and Ola,” in Time to Reshape the digital Society, 40th anniversary of the CRIDS, pp.321-338. 23. Lazcoz, G. & P. De Hert (2023), “Humans in the GDPR and AIA Governance of Automated and Algorithmic Systems: Essential Pre-requisites against Abdicating Responsibilities,” Computer Law & Security Review, 50, Art. 105833. 24. Levine, D. S. (2023), “Generative Artificial Intelligence and Trade Secrecy,” The Journal of Free Speech Law, 3(559), pp.559-590. 25. Li, W. & J. Toh (2023), “Data Subject Rights as a Tool for Platform Worker Resistance: Lessons from the Uber/Ola Judgments,” in Data Protection and Privacy, Volume 15: In Transitional Times, Hart Publishing, pp.119-156. 26. Lipton, Z. C. (2018), “The Mythos of Model Interpretability,” Communications of the ACM, 61(10), pp.36-43. 27. Lundberg, S. M. and Su-In Lee (2017),” A Unified Approach to Interpreting Model Predictions,” in 31st International Conference on Neural Information Processing Systems, pp.4765-4774. 28. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K. and Galstyan, A. (2021), “A Survey on Bias and Fairness in Machine Learning,” ACM Computing Surveys, 54(6), pp.1-35. 29. Mia, M. and Pritom, M. M. A. (2025), “Explainable but Vulnerable: Adversarial Attacks on XAI Explanation in Cybersecurity Applications,” arXiv:2510.03888. 30. Mishra, S., Dutta, S., Long, J. and Magazzeni, D. (2021), “A Survey on the Robustness of Feature Importance and Counterfactual Explanations,” arXiv:2111.00358. 31. Mothilal, R. K., Sharma, A. and Tan, C. (2020), “Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations,” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp.607-616. 32. Ribeiro, M. T., Singh, S. and Guestrin, C. (2016), ““Why Should I Trust You?”: Explaining the Predictions of Any Classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1135-1144. 33. Rudin, C. (2019), “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead,” Nature Machine Intelligence, 1(5), pp.206-215. 34. Saleh, K. and Abdulsalam, H. M. (2025), “Operationalizing ISO/IEC 42001: Requirements and Conformance Evidence for AI Management Systems,” in 2025 International Conference on AI for Sustainable Innovation (AI-SI). 35. Salih, A. M., Zahra, Raisi-Estabragh, Ilaria, B. G., Petia, R., Steffen E. P., Karim, L. and Gloria, M. (2025), “A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME,” Advanced Intelligent Systems, 7(1), 2400304. 36. Samek, W., Binder, A., Montavon, G., Lapuschkin, S. and Müller, Klaus-Robert (2017), “Evaluating the Visualization of What a Deep Neural Network has Learned,” IEEE Transaction on Neural Networks and Learning Systems, 28(11), pp.2660-2673. 37. Seshakagari, Haranadha Reddy Busireddy and Deventhira HariramNathan (2024), “AI-Augmented Fraud Detection and Cybersecurity Framework for Digital Payments and E-Commerce Platforms”. 38. Slack, D., Hilgard, S., Lakkaraju, H. and Singh, S. (2021), “Counterfactual Explanations can be Manipulated,” in 34 Advances in Neural Information Processing Sys. 39. Stigler, G. J. (1971), “The Theory of Economic Regulation,” The Bell Journal of Economy and Management Science, 2(1), pp.3-21. 40. Sundararajan, M., Taly, A. and Yan, Q. (2017), “Axiomatic Attribution for Deep Networks,” in Proceedings of the 34th International Conference on Machine Learning, pp.3319-3328. 41. Sylvain, O. (2018), “Discriminatory Designs on User Data,” Knight First Amendment Institute Emerging Threats Series, Vol.4, pp.1-30. 42. Wachter, S., Mittelstadt, B. and Russell, C. (2018), “Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR,” Harvard Journal of Law & Technology, Vol.31, pp.841-887. 43. Wachter, S., Mittelstadt, B. and Floridi, L. (2017), “Why a Right to Explanation of Automated Decision-Making does not Exist in the General Data Protection Regulation,” International Data Privacy Law, Vol.7, pp.76-99. 44. Wiegreffe, S. and Pinter, Y. (2019), “Attention is not not Explanation,” in Proceedings of EMNLP-IJCNLP 2019, pp.11-20. （三）政府文件與司法判決 1. Case C-634/21, SCHUFA Holding AG v. Land Hessen (OQ), ECLI:EU:C:2023:957 (Dec. 7, 2023).（自動化決策概念之目的論擴張解釋） 2. Case C-203/22, CK v. Magistrat der Stadt Wien (Dun & Bradstreet Austria), ECLI:EU:C:2025:117 (Feb. 27, 2025).（解釋權「相關邏輯有意義資訊」之四要件具體化） 3. Gonzalez v. Google LLC, 598 U.S. 617 (2023) (per curiam).（聯邦最高法院就 Section 230 對推薦演算法之適用爭議判決） 4. Moody v. NetChoice, LLC, No. 22-277, slip op. (U.S. July 1, 2024) (Kagan, J., opinion of the Court) (consolidated with No. 22-555, NetChoice v. Paxton).（聯邦最高法院就佛州 SB 7072 與德州 HB 20 之合憲性審查） 5. Federal Trade Commission v. Rite Aid Corp., No. 2:23-cv-05023 (E.D. Pa. Dec. 19, 2023).（FTC 就 Rite Aid 之臉部辨識 AI 系統失靈執法案） 6. European Parliament Research Service (EPRS), Artificial Intelligence and Civil Liability: Legal Affairs Study (Jul. 2020). 7. BAE Systems Applied Intelligence Labs, LTI Synthetic Data Technical Report, ASC 0259 D3 V1.2 (Jun. 8, 2020) (Brijesh Patel, Gary Francis, Indika Wanninayake, Alan Pilgrim & Ben Upton authors) (Contract No. DSTL/AGR/000616/01, Task 259, Study 3, © Crown Copyright 2020). 8. Draghi, Mario, The Future of European Competitiveness - Part A: A Competitiveness Strategy for Europe 8, 68-69 (2024). 9. Draghi, Mario, The Future of European Competitiveness - Part B: In-depth Analysis and Recommendations 308-318 (2024). 10. White House Office of Science and Technology Policy, Blueprint for an AI Bill of Rights (Oct. 2022). 11. Office of Management and Budget, Memorandum M-24-10, Advancing Governance, Innovation, and Risk Management for Agency Use of Artificial Intelligence (Mar. 28, 2024). 12. U.S. Department of Housing and Urban Development, Fair Housing Act Enforcement Prioritization (2025). 13. Consumer Financial Protection Bureau, Circular 2022-03, Adverse Action Notification Requirements in Connection with Credit Decisions Based on Complex Algorithms (May 26, 2022). 14. Press Release, European Commission, Commission Preliminarily Finds TikTok and Meta in Breach of Their Transparency Obligations under the Digital Services Act, IP/25/2503 (Oct. 24, 2025). （四）國際組織 1. IEEE 7001-2021, “IEEE Standard for Transparency of Autonomous Systems,” (Mar. 2021).（自動化系統透明度之工程標準；建立透明度對象之五層分層）。 2. ISO/IEC 27001:2022, “Information Security Management System (ISMS) – Requirements,” (Oct. 2022). 3. ISO/IEC 27002:2022, “Information Security, Cybersecurity and Privacy Protection - Information Security Controls,” (Feb. 2022). 4. ISO/IEC 27701:2019, “Security Techniques - Extension to ISO/IEC 27001 and ISO/IEC 27002 for Privacy Information Management - Requirements and Guidelines,” (Aug. 2019). 5. ISO/IEC 42001:2023, “Information Technology - Artificial Intelligence - Management System Requirements,” (Dec. 2023). 6. ISO/IEC 23894:2023, “Information Technology - Artificial Intelligence - Guidance on Risk Management,” (Feb. 2023). 7. National Institute of Standards & Technology, “Artificial Intelligence Risk Management Framework,” (AI RMF 1.0), NIST AI 100-1, (Jan. 2023). 8. National Institute of Standards & Technology, “Generative Artificial Intelligence Profile,” NIST AI 600-1, (Jul. 2024). 9. National Institute of Standards & Technology, “AI RMF Playbook,” (Jan. 26, 2023). 10. National Institute of Standards & Technology, “Guidelines for Evaluating Differential Privacy Guarantees,” NIST SP 800-226, (Mar. 2025) (Joseph P. Near, David Darais, Naomi Lefkovitz & Gary S. Howarth authors), https://doi.org/10.6028/NIST.SP.800-226. 11. Infocomm Media Development Authority (IMDA, Singapore), “Model AI Governance Framework,” (1st ed., Jan. 2019; 2nd ed., Jan. 2020). 12. Infocomm Media Development Authority (IMDA, Singapore), “AI Verify Framework: A Testing Framework and Software Toolkit,” (May 2022; AI Verify Foundation 接續開發). 13. Infocomm Media Development Authority (IMDA, Singapore), “Model AI Governance Framework for Generative AI,” (May 2024). 14. Infocomm Media Development Authority (IMDA, Singapore), “Model AI Governance Framework for Agentic AI,” (Jan. 2026). 15. Personal Data Protection Commission (PDPC, Singapore), “Advisory Guidelines on Use of Personal Data in AI Recommendation and Decision Systems,” (Mar. 2024). 16. Monetary Authority of Singapore (MAS), “Principles to Promote Fairness, Ethics, Accountability and Transparency (FEAT Principles) in the Use of Artificial Intelligence and Data Analytics in Singapore's Financial Sector,” (Nov. 2018). 17. Monetary Authority of Singapore (MAS), “Veritas Initiative White Papers on FEAT Methodology,” (2020-2022). 18. European Commission, “High-Level Expert Group on Artificial Intelligence (HLEG), Ethics Guidelines for Trustworthy AI,” (Apr. 8, 2019). 19. European Commission, “High-Level Expert Group on Artificial Intelligence (HLEG), The Assessment List for Trustworthy Artificial Intelligence,” (ALTAI) (July 17, 2020). 20. Organisation for Economic Co-operation and Development (OECD), “Recommendation of the Council on Artificial Intelligence,” OECD/LEGAL/0449, (May 22, 2019). 21. G7 Hiroshima AI Process, “International Guiding Principles for Organizations Developing Advanced AI Systems and International Code of Conduct for Organizations Developing Advanced AI Systems,” (Oct. 30, 2023). 22. UK Government, “Bletchley Declaration: AI Safety Summit Statement,” (Nov. 1-2, 2023). 23. Republic of Korea Government & UK Government, “Seoul Declaration on Safe, Innovative and Inclusive AI; Seoul Statement of Intent toward International Cooperation on AI Safety Science,” (May 21-22, 2024). 24. National Institute of Standards and Technology, “U.S. Artificial Intelligence Safety Institute,” (NIST AISI) (Established Feb. 7, 2024). 25. UK Government Department for Science, “Innovation and Technology,” UK AI Safety Institute, (Established Nov. 2, 2023). 26. Information-technology Promotion Agency, “Japan, Japan AI Safety Institute,” (AISI Japan) (Established Feb. 14, 2024). 27. International Network of AI Safety Institutes, “Joint Statement on the International Network of AI Safety Institutes,” (Nov. 2024). （五）網路資源 1. AI Verify Foundation, “Project Moonshot,” https://aiverifyfoundation.sg/project-moonshot/ (last visited Apr. 25, 2026). 2. AI Verify Foundation, “AI Verify Open-Source Toolkit (GitHub Repository),” https://github.com/aiverify-foundation/aiverify (last visited Apr. 25, 2026). 3. Amazon Seller Central, “Amazon Services Business Solutions Agreement §§ 1-3,” https://sellercentral.amazon.com/help/hub/reference/external/G1791?mons_sel_locale=zh_TW (last visited May 2, 2026). 4. Meta, “Meta Launches New Anti-Scam Tools, Deploys AI Technology to Fight Scammers and Protect People (Nov. 6, 2024),” https://about.fb.com/news/2024/11/meta-launches-new-anti-scam-tools-deploys-ai-technology-to-fight-scammers-and-protect-people/ (last visited Apr. 25, 2026). 5. Rizzatti, L., “A closer look at LLM’s hyper growth and AI parameter explosion,” EDN, https://www.edn.com/a-closer-look-at-llms-hyper-growth-and-ai-parameter-explosion/ (last visited May 10, 2026). 6. arXiv, https://arxiv.org/. 7. SSRN（Social Science Research Network）, https://papers.ssrn.com/. 8. Google Scholar, https://scholar.google.com/. 9. ResearchGate, https://www.researchgate.net/. 10. ACM Digital Library, https://dl.acm.org/. 11. IEEE Xplore, https://ieeexplore.ieee.org/. 12. AI Verify Foundation, “GitHub Organization,” https://github.com/aiverify-foundation/. 13. Microsoft Responsible AI Toolbox, “GitHub Repository,” https://github.com/microsoft/responsible-ai-toolbox/. 14. Christoph Molnar, “Interpretable Machine Learning Book (Online),” https://christophm.github.io/interpretable-ml-book/. 15. LIME GitHub Repository, https://github.com/marcotcr/lime/. 16. SHAP GitHub Repository, https://github.com/shap/shap/. （六）其他 1. Cybersource (2025), “2025 Global eCommerce Payments & Fraud Report.” 2. Cybersource, Merchant Risk Council (2023), “Verifi & B2B International,” 2023 Global eCommerce Payments and Fraud Report. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/104117 | - |
| dc.description.abstract | 我國電子商務活動於2020年代持續擴張，隨之衍生之詐欺犯罪已成為影響數位經濟秩序之問題。主流電商平台已自人工審查為主之防詐模式轉向以人工智慧（AI）系統為主之自動化治理；惟AI系統之黑箱特性使既有法制於透明性義務之具體化上面臨結構性挑戰。我國雖於 2024 年 7 月公布施行《詐欺犯罪危害防制條例》、於2026年1月14日公布施行《人工智慧基本法》，惟其透明性條款多止於原則宣示，子法訂定工作正進入關鍵 24 個月檢討期，立法建議與實踐途徑之時序急迫性具實務意義。
本論文以三組研究問題為核心：其一，於電商平台應用AI防詐時，《詐欺犯罪危害防制條例》第36條第1項所稱「善良管理人之注意義務」與「合理措施」為何；其二，於電商平台應用AI防詐時，其透明度義務之內涵具體內容為何；其三，針對我國電商平台應用AI防詐應負之透明度義務，相關監理框架應如何設計。 本論文考量技術可行性與規範明確性等面向後，參考國際組織、比較法以及相關技術標準後，提出六項具體立法建議：建議一，人工智慧基本法第 4 條第 5 款之透明度指引，建議由數位發展部依同法第 16 條第 1 項後段以「協助」定位草擬之，並賦予AI評測中心明確定位與角色；建議二，該指引應針對透明度之程度、時點及對象等三面向之具體要件有明文化規定；建議三，制定平台AI透明度專法，設「一般解釋權」與「反對自動化決策權」專章（個人資料保護法修法得為評估之替代途徑），配套規範資料保護影響評估（Data Protection Impact Assessment，DPIA）等具體要件，並參考歐盟Regulation（EU）2019/1150及人工智慧法第86條涵蓋對商業使用者（含3P企業賣家）之透明度規範，填補企業賣家救濟缺口；建議四，制定人工智慧基本法第 17 條高風險 AI 責任歸屬之子法，並由各目的事業主管機關依原有授權訂立各業別之特定AI管理規範；建議五，由公平交易委員會依公平交易法第25條之授權，就數位平台AI不透明決策之適用發布解釋令，擴張處理原則第7點第（四）款「不當利用相對市場優勢地位」至數位平台情境；建議六，建立跨國合規相容性政策，透過AI安全機構（AI Safety Institutes，AISI）國際合作、ISO/IEC 42001認證互認、經濟合作暨發展組織（OECD）與七大工業國組織（G7）政策對話等面向進行。 前揭六項建議將建立於四層立法架構：其一，由人工智慧基本法設定原則性框架與授權主管機關；其二，制定平台AI透明度專法建構對業者之解釋權基礎；其三，由數位發展部制定AI透明度指引作為對業者之非拘束性技術規範參考；其四，各目的事業主管機關依原有授權訂立各業別之特定 AI 管理規範。希冀於人工智慧基本法第18條規定之二年法規檢討期限屆滿前，為我國電商平台AI防詐治理法制建構提供方向性指引建議。 | zh_TW |
| dc.description.abstract | E-commerce activities in Taiwan have continued to expand throughout the 2020s, and the resulting fraud has become a structural challenge to the order of the digital economy. Mainstream e-commerce platforms have shifted from a fraud-prevention model centered on manual review to automated governance driven primarily by artificial intelligence (AI) systems; however, the black-box nature of such systems poses structural challenges to the concretization of transparency obligations under the existing legal framework. Although Taiwan promulgated the Fraud Crime Hazard Prevention Act in July 2024 and the Artificial Intelligence Basic Act on 14 January 2026, its transparency provisions remain at the level of principled declarations. As secondary legislation enters a critical 24-month review period, the timing urgency of legislative recommendations and implementation pathways carries substantive practical significance.
This thesis is organized around three sets of research questions: first, how the duty of care of a “good-faith administrator” and the notion of “reasonable measures” under Article 36, paragraph 1 of the Fraud Crime Hazard Prevention Act should be understood in AI-driven fraud prevention contexts; second, how the transparency obligations should be understood in the said context; and third, how to establish the regulatory framework for the transparency obligations of Taiwan’s e-commerce platforms in the said context. After considering aspects of technological feasibility and regulatory certainty and taking reference from international organizations, comparative laws, and technical standards, this thesis proposes six specific legislative recommendations: Recommendation 1-the Ministry of Digital Affairs may issue the transparency guideline under Article 4, Paragraph 5 of the Artificial Intelligence Basic Act per Article 16, Paragraph 1 as a non-binding, yet "supportive" instrument covering technical specifications, together with a clearly defined role for the AI Evaluation Center; Recommendation 2-the said guideline shall specify the specific requirements of the three dimensions of the transparency obligations, degree, timing, and subject matter; Recommendation 3-enacting a special Platform AI Transparency Act with dedicated chapters on the general right to explanation and the right to object to automated decision-making (an amendment to the Personal Data Protection Act as an alternative pathway), supplemented by Data Protection Impact Assessment (DPIA) requirements, and extending transparency obligations to business users (including 3P commercial sellers) by reference to Regulation (EU) 2019/1150 and Article 86 of the EU Artificial Intelligence Act, thereby filling the gap in remedies for commercial sellers; Recommendation 4-relevant competent authorities shall enact the regulation on high-risk AI liability attribution under Article 17 of the Artificial Intelligence Basic Act, with sector-specific AI administration rules; Recommendation 5-the Fair Trade Commission shall issue an interpretive order on the application of Article 25 of the Fair Trade Act to opaque AI decision-making on digital platforms, which extends Item (4) of Point 7 of its Enforcement Guidelines (“improper exploitation of relative market advantage”) to digital-platform scenarios; Recommendation 6-implementing cross-border regulatory harmonization policies through three parallel tracks: international cooperation among AI Safety Institutes (AISI), mutual recognition of ISO/IEC 42001 certifications, and policy dialogues with the OECD and G7. The six recommendations rest upon a "four-layer legislative architecture": (i) the Artificial Intelligence Basic Act establishes the framework and designates competent authorities; (ii) a special Platform AI Transparency Act establishes the right to explanation against operators; (iii) the AI Transparency Guidelines serve as non-binding technical reference standards for operators; and (iv) competent authorities issue sector-specific AI administration rules under their existing statutory mandates. It is anticipated that, prior to the expiration of the two-year regulatory review period under Article 18 of the Artificial Intelligence Basic Act, this thesis provides directional recommendations for the construction of AI-driven fraud prevention governance for Taiwan’s e-commerce platforms. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-08-21T16:47:51Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-08-21T16:47:51Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝誌 i
中文摘要 ii Abstract iv 目次 vi 表次 x 第一章 緒論 1 第一節 研究動機與目的 1 第一項 電商詐欺之實證規模與技術轉向 1 第二項 立法回應與規範空白 3 第三項 研究目的 5 第二節 研究問題與範圍 7 第一項 研究問題 7 第二項 研究範圍 9 第三項 核心概念之初步界定 10 第三節 研究方法 12 第一項 文獻分析與比較法研究法 12 第二項 案例分析法 12 第四節 研究架構 14 第二章 電商平台AI防詐之現況與規範架構 15 第一節 電商平台詐欺防制的義務、責任與合理措施 15 第一項 電商平台之商業模式與多邊關係 15 第二項 現行法義務規範未具體化 17 第三項 電商平台生態圈聯防責任與義務 17 第四項 合理措施與透明度 19 第五項 現行規範密度探究 20 第二節 運用AI進行詐欺防制的困境 22 第一項 技術困境 22 第二項 法律困境–營業秘密保護與基本權保障 24 第三項 治理困境 27 第三節 小結 29 第三章 AI透明度與可解釋性之技術與法律分析 30 第一節 透明度與可解釋性 30 第一項 透明度概念之多義性與規範定位 30 第二項 可解釋性概念之技術、法律雙重脈絡 31 第三項 解釋權概念之規範界線 35 第四項 透明度、可解釋性與解釋權之區辨與相互關係 36 第二節 AI黑箱特性與XAI的技術現況 38 第一項 黑箱性之三重特性 38 第二項 機器學習類型之透明度結構差異 40 第三項 XAI技術工具彙整 43 第四項 技術現況之規範意涵 48 第三節 可解釋性的技術測試方法、工具與可操作性 50 第一項 透明度技術評估之標準框架 50 第二項 可解釋性測試方法論 50 第三項 XAI工具之可操作性評估指標 52 第四項 Uber案AI子系統之忠實度框架檢驗 55 第五項 技術可行性原則與其立法定錨 59 第四節 AI透明度治理案例分析-Uber案 60 第一項 阿姆斯特丹上訴法院判決之事實 60 第二項 Uber服務條款分析 61 第三項 Uber案之評釋 63 第四項 以歐盟人工智慧法補充檢視 65 第五項 Uber案例對我國規範設計之啟示 66 第五節 可解釋程度與透明度 68 第一項 可解釋程度之層級化分析 68 第二項 治理證據鏈之概念建構 70 第三項 技術、法律解釋方法與責任歸屬 71 第六節 小結 74 第四章 比較法分析 75 第一節 歐盟、美國與法規透明度比較 75 第一項 歐盟法之透明度規範體系 75 第二項 美國法之透明度規範體系 83 第三項 歐美與我國立法比較分析 91 第二節 國際標準與業界規範 97 第一項 國際標準組織之AI治理框架 97 第二項 業界與監管科技定錨標準 100 第三項 國際標準於我國法之銜接適用 103 第三節 小結 107 第五章 立法建議與電商平台因應策略 108 第一節 規範不足之分析與因應 108 第二節 立法建議 113 第一項 規範密度提升 113 第二項 透明度要求具體化 115 第三項 平台規模差異化 125 第四項 跨國合規相容性 125 第三節 電商平台因應策略與實務 128 第一項 電商平台AI防詐之治理策略架構 128 第二項 1P/2P/3P與混合模式之差異化治理實務 131 第四節 實踐挑戰與動態調整 137 第六章 結論 140 參考文獻 142 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | AI透明性義務 | - |
| dc.subject | 黑盒子 | - |
| dc.subject | XAI | - |
| dc.subject | LIME | - |
| dc.subject | SHAP | - |
| dc.subject | 反事實解釋 | - |
| dc.subject | 電商平台AI防詐 | - |
| dc.subject | 人工智慧基本法 | - |
| dc.subject | 解釋權 | - |
| dc.subject | AI治理證據鏈 | - |
| dc.subject | 透明度框架 | - |
| dc.subject | AI transparency obligation | - |
| dc.subject | Black box | - |
| dc.subject | Explainable AI (XAI) | - |
| dc.subject | LIME | - |
| dc.subject | SHAP | - |
| dc.subject | Counterfactual explanation | - |
| dc.subject | E-commerce AI-driven fraud prevention | - |
| dc.subject | Artificial intelligence basic act | - |
| dc.subject | Right to explanation | - |
| dc.subject | AI governance evidence chain | - |
| dc.subject | Transparency framework | - |
| dc.title | 人工智慧透明性義務之法制研究-以電商平台詐欺防制為中心 | zh_TW |
| dc.title | A Legal Study on the Transparency Obligations of Artificial Intelligence: Focusing on Fraud Prevention in E-commerce Platforms | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林勤富;黃詩淳 | zh_TW |
| dc.contributor.oralexamcommittee | Ching-Fu Lin;Sieh-chuen Huang | en |
| dc.subject.keyword | AI透明性義務; 黑盒子; XAI; LIME; SHAP; 反事實解釋; 電商平台AI防詐; 人工智慧基本法; 解釋權; AI治理證據鏈; 透明度框架 | zh_TW |
| dc.subject.keyword | AI transparency obligation; Black box; Explainable AI (XAI); LIME; SHAP; Counterfactual explanation; E-commerce AI-driven fraud prevention; Artificial intelligence basic act; Right to explanation; AI governance evidence chain; Transparency framework | en |
| dc.relation.page | 152 | - |
| dc.identifier.doi | 10.6342/NTU202603297 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2026-08-11 | - |
| dc.contributor.author-college | 進修推廣學院 | - |
| dc.contributor.author-dept | 事業經營法務碩士在職學位學程 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 事業經營法務碩士在職學位學程 | |
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| ntu-114-2.pdf 未授權公開取用 | 3.18 MB | Adobe PDF |
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