Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
<|reference_start|>Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI: In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and protection that impact professionals across various careers. With this in mind, this survey provides an extensive examination of copyright infringement as it pertains to generative AI, aiming to stay abreast of the latest developments and open problems. Specifically, it will first outline methods of detecting copyright infringement in mediums such as text, image, and video. Next, it will delve an exploration of existing techniques aimed at safeguarding copyrighted works from generative models. Furthermore, this survey will discuss resources and tools for users to evaluate copyright violations. Finally, insights into ongoing regulations and proposals for AI will be explored and compared. Through combining these disciplines, the implications of AI-driven content and copyright are thoroughly illustrated and brought into question.<|reference_end|>
@article{dzuong2024uncertain,
    title={Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues
  in Generative AI},
    author={Jocelyn Dzuong, Zichong Wang, Wenbin Zhang},
    journal={arXiv preprint arXiv:2404.08221},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08221},
    primaryClass={cs.LG cs.AI cs.CY}
}