人工智慧模型訓練之著作權侵權爭議研究

Abstract

人工智慧模型需以大量資料進行訓練，倘訓練資料中包含受著作權保護之著作，且未取得著作權人授權，恐有侵害重製權之虞，目前美國已累積40餘起生成式人工智慧模型相關著作權侵權案件，涵蓋文字、圖像、音樂等多種內容領域，加拿大、歐盟、中國及印度等地亦有零星訴訟發生，如何因應科技發展與著作權保護間之衝突已成為各國政府關注的焦點。礙於繁體中文資料之缺乏，我國生成式人工智慧模型發展稍嫌落後，且我國著作權案件經常與刑罰相繩，因此如何適度調整我國著作權法，衡平開發者與著作人間之利益，已屬刻不容緩之議題。 在國外立法例上，美國著作權法未針對本議題進行修法，仍一貫以合理使用及「轉化性使用」框架解決新興科技面臨之著作權爭議，而日本及歐盟則採權利限制規定，2018年日本著作權法修法採取三層次靈活的權利限制規定，其中第30條之4規範「非以享受作品中表達之思想或情感為目的」且「未不當損害著作權人利益」者，即可於未經權利人授權下進行資訊分析；歐盟數位單一市場著作權指令則將文字資料探勘分為兩種類型，於一般資料探勘賦予權利人選擇退出權利，各國法制各有其值得參考之處及適用上可能面臨之障礙。 針對我國著作權法未來走向，本文以三種途徑分析其可行性：維持現行法、以歐盟及日本法為基礎增訂規範、建立法定授權制度，並就政府角色和著作權法之調整提出以下建議：（一）政府應審視文化相關預算分配與金額，建立訓練資料平台系統，促進繁體中文資料之蒐集、管理、利用與流通。（二）關於我國著作權法調整，本文認為有兩種可行途徑，惟何種方向更適宜我國之政策目標及利益衡平，宜由相關部門進行更多實證調查：一為維持既有合理使用規範，限縮著作權刑事責任之適用；二為於著作權法第51條之1新增資訊分析合理使用規定，並可參考歐盟人工智慧辦公室提供之模板和施行成效，於第64條規範透明性義務。

Artificial intelligence (AI) models require large amounts of data for training. If such training data contain copyrighted works without authorization from the copyright owners, there is a risk of infringing the reproduction right. In the United States, more than forty copyright infringement lawsuits related to generative AI models have been filed, covering diverse domains such as text, images, and music. Sporadic cases have also arisen in Canada, the European Union, China, and India. How to reconcile the tension between technological development and copyright protection has become a focal issue for governments worldwide. Due to the scarcity of traditional Chinese-language datasets, the development of generative AI models in Taiwan has lagged somewhat behind, and because copyright disputes in Taiwan are often accompanied by criminal penalties, the question of how to appropriately amend the Copyright Act to balance the interests between developers and copyright owners has become a matter of pressing urgency. From a comparative legal perspective, U.S. copyright law has not been amended to address this issue, continuing instead to resolve copyright disputes arising from emerging technologies through the frameworks of fair use and “transformative use.” By contrast, Japan and the European Union have adopted limitations of copyright. The 2018 amendment to Japan’s Copyright Act introduced a flexible, three-tiered limitations regime, under which Article 30-4 permits data analysis without the authorization of the copyright owner when the purpose is “not to enjoy the thoughts or emotions expressed in the work” and such use “does not unduly prejudice the interests of the copyright owner.” The EU Directive on Copyright in the Digital Single Market distinguishes between two types of text and data mining (TDM), granting copyright owners an opt-out right for general TDM. Each legal framework offers valuable reference points, as well as potential obstacles to application. This paper evaluates three potential approaches for the future direction of Taiwan’s Copyright Act: (1) retaining the current legal framework; (2) introducing new provisions based on the EU and Japanese models; and (3) establishing a statutory licensing system. It further offers the following recommendations concerning the government’s role and legislative adjustments: (1) The government should review budget allocations for cultural initiatives and establish a centralized training data platform to facilitate the collection, management, utilization, and circulation of traditional Chinese-language datasets; (2) With respect to amending the Copyright Act, two feasible approaches exist, and determining which is more suitable for Taiwan’s policy objectives and interest-balancing should be based on further empirical research by relevant authorities: one is to retain the current fair use provision while narrowing the scope of criminal liability under copyright law; the other is to add a data analysis fair use provision to Article 51-1, drawing on the templates and implementation outcomes provided by the EU AI Office, and to incorporate transparency obligations under Article 64.