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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Legal Studies in Digital Age</JournalTitle>
      <Issn></Issn>
      <Volume>2</Volume>
      <Issue>Serial Number 3</Issue>
      <PubDate PubStatus="epublish">
        <Year>2023</Year>
        <Month>04</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>The Future of Intellectual Property in AI-Generated Creativity: Authorship, Originality, and Protection</ArticleTitle>
    <VernacularTitle>The Future of Intellectual Property in AI-Generated Creativity: Authorship, Originality, and Protection</VernacularTitle>
    <FirstPage>49</FirstPage>
    <LastPage>60</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2023</Year>
        <Month>02</Month>
        <Day>11</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;The rise of generative artificial intelligence has transformed the nature of creative production, forcing a fundamental re-evaluation of the intellectual property frameworks that govern authorship, originality, and ownership. As large language models, image generators, and multimodal systems increasingly produce complex and aesthetically refined works, traditional copyright doctrines—grounded in human intentionality and expressive labor—struggle to accommodate outputs generated through autonomous or semi-autonomous computational processes. This narrative review synthesizes current legal, ethical, and policy debates to examine how AI-generated creativity challenges long-standing assumptions about what qualifies as a creative work and who may claim authorship or control. The article explores the conceptual divide between human–AI co-creation and fully automated generation, the shifting boundaries of human contribution, and the emerging complexities of originality in contexts where outputs may be statistically novel yet lack human expressive judgment. It analyzes the growing difficulty of evaluating derivative works, substantial similarity, and accidental replication within algorithmic generation, highlighting unresolved tensions in applying traditional infringement standards to non-human creative systems. The review also assesses national regulatory approaches, including developments in the United States, European Union, United Kingdom, Singapore, and China, alongside broader international harmonization challenges involving cross-border disputes and enforcement gaps. Finally, the article discusses future pathways for intellectual property protection, including proposals for AI-assisted authorship categories, hybrid rights frameworks, presumptive human ownership, and transparency obligations for generative systems. By synthesizing these threads, the review underscores the need for adaptable, ethically grounded, and technologically informed legal reforms capable of addressing the evolving dynamics of creativity in an AI-driven era.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">AI-generated creativity</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">intellectual property</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">authorship</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">originality</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">copyright</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">generative AI</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">machine learning</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">derivative works</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">policy frameworks</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">digital governance</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.jlsda.com/index.php/lsda/article/download/307/249</ArchiveCopySource>
  </Article>
</ArticleSet>
