Originality Score

Originality scoring aims to quantify the novelty and uniqueness of content generated by artificial intelligence models, particularly in text and image generation, addressing crucial copyright and authenticity concerns. Current research focuses on developing metrics that align with legal definitions of originality, often leveraging techniques like textual inversion and latent space analysis within transformer-based language models and generative diffusion models to assess the complexity and unexpectedness of generated outputs. These efforts are significant for mitigating copyright infringement in AI-generated content and for developing more robust methods to distinguish between human- and AI-generated text, impacting both legal frameworks and the broader understanding of creativity and authenticity in the digital age.