Key points are not available for this paper at this time.
Since the 2010s, artificial intelligence (AI) has quickly grown from another subset of machine learning (ie deep learning) in particular with recent advances in generative AI, such as ChatGPT. The use of generative AI has gone beyond leisure purposes. It has now been widely used to generate music, news articles and image-based art works. This prompts a regulatory interpretation as to how AI-generated works should be appropriately used to eliminate their potential harm to society, but at the same time how it should be protected to foster human creativity and promote a well-functioning market. This article is an update from the author’s evidential report and speech on “AI and Intellectual Property Rights: IPR Protection for AI-Created Work” for the evidence meeting of the All-Party Parliamentary Group on Artificial Intelligence on 24 January 2022. It considers whether AI technologies should be granted status as copyright or patent owners by looking into existing regulations in the United Kingdom, European Union, United States and China. It further considers how generative AI copyright protection should be managed in the digital society to protect users and strike a fair balance among rightsholders. It argues that it would be beneficial to a well-functioning market if AI-generated works could be subject to collective management of copyright via copyright management organizations within countries. In addition, the article provides mapping of existing legislations in a comparative study and their interpretation for the application of AI-generated works protection and aims to bring together global policymakers and stakeholders to initiate joint efforts to promote international harmonization on intellectual property rights (IPR) protection for AI-generated works. Keywords: artificial intelligence; generative AI; AI-generated works; collective copyright management; computer-generated work; copyright protection.
Building similarity graph...
Analyzing shared references across papers
Loading...
Faye F Wang
Brunel University of London
Amicus Curiae
Brunel University of London
Building similarity graph...
Analyzing shared references across papers
Loading...
Faye F Wang (Fri,) studied this question.
synapsesocial.com/papers/6a181f631723722a886f4ff3 — DOI: https://doi.org/10.14296/ac.v5i1.5663
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: