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This study introduces a new genericization method to reduce copyright infringement risks in generative AI. The PREGen technique significantly lowers the chances of AI generating copyrighted characters, promoting responsible AI development.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Intellectual Property Law

Background:

  • Generative AI's rapid advancement raises significant copyright concerns, evidenced by numerous lawsuits.
  • AI models can generate images of copyrighted characters, posing legal and ethical challenges.
  • Existing copyright mitigation techniques for AI have limitations, leaving substantial risks.

Purpose of the Study:

  • To propose a novel genericization method for generative AI outputs to mitigate copyright infringement.
  • To develop a metric for quantifying data originality within generative models.
  • To introduce PREGen (Prompt Rewriting-Enhanced Genericization) as a practical implementation.

Main Methods:

  • Developed a genericization technique to modify AI model outputs, reducing imitation of copyrighted features.
  • Introduced a metric to quantify data originality by sampling from generative models.
  • Implemented PREGen by combining the genericization method with an existing technique.

Main Results:

  • PREGen reduced the generation of copyrighted characters by over 50% when character names were used in prompts.
  • PREGen nearly eliminated the generation of copyrighted characters when names were not explicitly mentioned.
  • The developed originality metric was applied effectively within the genericization process.

Conclusions:

  • The proposed genericization method and PREGen offer practical solutions for strengthening copyright protection in generative AI.
  • This research advances computational approaches for responsible AI development and copyright compliance.
  • The findings provide methodologies to reduce the likelihood of AI generating infringing content.