数据与合理使用

Yung-Myung Kim
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引用次数: 1

摘要

数据的收集和使用是机器学习的开始和结束。看看ChatGPT,数据正在使机器与人类的能力相媲美。在判断为系统学习生产或保护数据的过程是否合理使用时,商业目的不会自然被拒绝。英国、德国和欧盟(EU)也在为研究等非营利性目的的数据挖掘引入版权限制,日本更为积极。虽然没有像韩国和美国那样制定全面的合理使用规定,但日本的积极立法表明了引领人工智能产业的意愿。2020年,韩国提出了限制信息分析的《著作权法》修订案。它将能够提高运营商的可预测性。然而,该修正案的立法预计将遭到权利人的反对,可能需要时间。因此,本文考察了数据爬行和TDM等机器学习是否符合现行版权法下的合理使用。综上所述,认为它可能对应于合理使用,理由是它不同于人类的使用行为。但是,经营者按照合理使用的原则使用他人的作品,将所有的排他性过失都归为经营者是否合理,值得商榷。对于经营者通过使用TDM或机器学习产生的机器作品获得利润的补偿制度,不能排除对公平竞争环境造成严重后果的可能性。
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Data and Fair use
Data collection and use are the beginning and end of machine learning. Looking at ChatGPT, data is making machines comparable to human capabilities. Commercial purposes are not naturally rejected in the judgment of fair use of the process of producing or securing data for system learning. The UK, Germany, and the EU are also introducing copyright restrictions for data mining for non-profit purposes such as research studies, and Japan is more active. Japan’s active legislation is the reason why there are no comprehensive fair use regulations like Korea and the United States, but it shows its willingness to lead the artificial intelligence industry. In 2020, a revision to the Copyright Act was proposed in Korea to introduce restrictions for information analysis. It will be able to increase the predictability for operators. However, the legislation of the amendment is expected to be opposed by the right holder and may take time. Therefore, it was examined whether machine learning such as data crawling and TDM corresponds to fair use through fair use under the current copyright law. In conclusion, it was considered that it may correspond to fair use, citing that it is different from human use behavior. However, it is questionable whether it is reasonable to attribute all exclusive negligence to the business operator by using the works of others according to fair use. The reason why the compensation system for profits earned by operators through the use of machine works generated by TDM or machine learning cannot be excluded from the possibility of serious consequences for a fair competitive environment.
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