Artificial Intelligence Produced Original Work: A New Approach to Copyright Protection and Ownership

Atif Aziz

European Journal of Artificial Intelligence and Machine Learning, 2023, vol. 2, issue 2, 9-16

Abstract: The journey of copyright protection that started with the printing press in the 16th century entered a new era of challenges with the technological advancement of the 21st century. Copyright has rights and enforcement that are grounded in legislative enactments. This paper advocates that A. I.-produced work is original and deserves copyright protection. Artificial Intelligence (A. I.) has emerged as a powerful technology that has enabled the creation and assimilation of new and unique authorship. The amount of work that A. I. is producing in the fields of science, medicine, art, law, and literature is increasing dramatically. This paper addresses the question of why A. I. generated work deserves copyright protection and how it correlates with its ownership. A comparative analysis of the existing copyright laws in various jurisdictions is examined. A rundown of current challenges of digital copyright and future developments are discussed. The paper presents the idea of legal personhood and how it correlates with copyright work ownership. Five traditional ownership options are compared and considered. A hybrid ownership model that gives legal personality to the artificial intelligence (AI) system, its programmer, user, and the company under the umbrella of a legal entity like artificial personality (AiLE) is proposed. In most jurisdictions, legislative changes are required to address and provide a new foundation for copyright protection and ownership of AI. -produced original work. Hence, the need to address the current challenges of digital copyright and its rightful owner is essential in unleashing the true potential and further development of A. I.

Keywords: A. I. generated work; AI legal entity; AI work ownership; copyright for A. I; protection of A. I. (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejai/article/view/1015 Abstract page (text/html)
https://eu-opensci.org/index.php/ejai/article/download/1015/346 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejai00:v:2:y:2023:i:2:id:1015

DOI: 10.24018/ejai.2023.2.2.15

Access Statistics for this article

More articles in European Journal of Artificial Intelligence and Machine Learning from European Open Science
Bibliographic data for series maintained by Support Team ().