
Generative AI is transforming creative industries by merging advanced algorithms with human creativity. This paper examines the legal and ethical challenges of applying U.S. copyright law to AI-generated content. Using qualitative analysis of legal cases and a review of user-generated AI prompt data, we identify key issues regarding human authorship and fair use. This paper delves into the multifaceted ethical terrain of GAI, examining the perspectives of various stakeholders, including copyright holders, artists, and end users. It explores the ramifications of machine learning and training data on individual rights and innovation, striving to strike a balance that fosters progress while upholding artistic expression. In examining user-generated prompts submitted to the Midjourney platform, this study applied a qualitative coding framework to categorize prompts according to thematic elements, including artistic style, subject complexity, and apparent intent regarding originality. The coding scheme was developed inductively through an initial review of a representative sample of prompts and refined through iterative discussion among the authors. To enhance reliability, cross-validation was undertaken by independently coding a subset of prompts and reconciling discrepancies through consensus. This methodological approach seeks to provide insight into the emergent creative roles of AI users and their implications for copyright analysis. Nurturing open dialogue between proponents of change and preservation is crucial for addressing legal, ethical, and societal challenges, thereby harnessing the potential of generative artificial intelligence while safeguarding creators' rights and nurturing human creativity. Furthermore, this paper delves into the intersection of generative AI and copyright law, examining two fundamental questions: the copyrightability of generative AI-generated expression and the extent to which generative AI platforms and content may constitute copyright infringement or other violations. By exploring these questions, it contributes to the ongoing discourse surrounding the legal and ethical implications of this groundbreaking technology. Our findings indicate that current legal frameworks require clearer guidelines to balance innovation with the protection of creative rights.
training data, generative artificial intelligence (GAI), copyright law, text-to-image AI, copyright infringement, fair use, legal challenges
training data, generative artificial intelligence (GAI), copyright law, text-to-image AI, copyright infringement, fair use, legal challenges
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