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資料權利之探討-從發展人工智慧需求出發

Rethink Data Policy: From the Development of Artificial Intelligence

摘要


WIPO理事長Francis Gurry於解碼人工智慧論壇表示:「在機器學習和其他人工智慧發展會仰賴大量資料,而我們目前最大的挑戰來自於資料,我們可以從不同角度看到人工智慧政策與資料交疊。」資料是多數人工智慧科技的原料,與資料相關的政府政策,將密切影響人工智慧的發展。人工智慧業者期待政府的資料政策能確保資料可以自由跨境傳輸、獲得存取政府資料管道、促進資料增值服務、保持競爭政策的可預測性。目前我國與其他多數國家的施政方針是致力於予以「存取政府資料管道」並且以資料利用與開放政策「促進資料增值服務」,如:歐盟《開放資料指引》、美國《開放、公開、電子化與必要政府資料法》、中國大陸《促進大數據發展行動綱要》、我國《ide@Taiwan 2020創意臺灣政策白皮書》,各個政府開放政策都有相應的授權條款。多國政府現行開放授權條款則與「創用CC」之原則相類,其背後又有資料與智慧財產權的省思。本研究擬瞭望各國公部門的資料政策和相應的授權規範、省思資料在法律上的權利,期待提供人工智慧政策作為參酌。

關鍵字

人工智慧 資料 開放資料 創用CC

並列摘要


"Data is where our greatest challenges lie. We see the multiplicity of policy dimensions intersect around data," WIPO Director General Gurry mentioned in an artificial intelligence (AI) conference hosted by WIPO. As the innovation of AI is driven by a vast amount of data, the data policy influences the AI development in various aspects. AI developers and entrepreneurs urge the government to ensure the data can transmit freely across the borders, to open up the access to government data and public sector information, to facilitate value-added data services, and to maintain predictable competition policies. Many countries provide access to government data to reconcile the imperative of openness with innovation. On the other hand, closure is needed. To balance the need for openness and closure, governments update the data policies and the licensing agreements. Notably, Taiwan, as well as many other countries, has implemented the licensing policies similar to Creative Commons (CC) Attribution 4.0 International. Creative Commons is a project initiated by Professor Lawrence Lessig from Stanford Law School. Plentiful intellectual property issues lie under the Creative Commons and data licensing. Focusing on data, this research aims to provide an overview of the AI policy trend and the licensing policy.

參考文獻


資料權利之探討-從發展人工智慧需求出發,朱翊瑄撰,載於科技法律透析,2020年10月,第32卷第10期,第29~50頁。投稿日:2020年4月28日;接受刊登日:2020年7月28日。
WORLD ECONOMIC FORUM, The Global Risks Report 2020 (January 15, 2020), 67, available at https://www.weforum.org/reports/the-global-risks-report-2020 (last visited Jan. 17, 2020).
Michael Copeland, What's the Difference Between Artificial Intelligence, Machine Learning and Deep Learning?, NVIDIA Blogs, https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/ (last visited Apr. 18, 2020)
ARTUR KIULIAN, ROBOT IS THE BOSS: HOW TO DO BUSINESS WITH ARTIFICIAL INTELLIGENCE, 17-20 (2017).
Isha Salian,〈監督式學習、非監督式學習、半監督式學習與強化學習這四者間的區別〉,NVIDIA 官方部落格,2018/09/04,https://blogs.nvidia.com.tw/2018/09/supervised-unsupervised-learning/ (最後瀏覽日:2020/01/30)。

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