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Not All Similarities Are Created Equal: Leveraging Data-Driven Biases to Inform GenAI Copyright Disputes
March 27, 2024, 4:46 a.m. | Uri Hacohen, Adi Haviv, Shahar Sarfaty, Bruria Friedman, Niva Elkin-Koren, Roi Livni, Amit H Bermano
cs.CV updates on arXiv.org arxiv.org
Abstract: The advent of Generative Artificial Intelligence (GenAI) models, including GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content creation, enabling non-professionals to produce high-quality content across various domains. This transformative technology has led to a surge of synthetic content and sparked legal disputes over copyright infringement. To address these challenges, this paper introduces a novel approach that leverages the learning capacity of GenAI models for copyright legal analysis, demonstrated with GPT2 and Stable Diffusion …
abstract artificial artificial intelligence arxiv biases copilot copyright cs.cl cs.cv data data-driven diffusion domains enabling genai generative generative artificial intelligence github gpt intelligence openai openai gpt professionals quality stable diffusion synthetic technology type
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