Artificial Intelligence (AI), and in particular generative models, are transformative tools for knowledge work. They problematise notions of creativity, originality, plagiarism, the attribution of credit, and copyright ownership. Critics of generative models emphasise the reliance on large amounts of training data, and view the output of these models as no more than randomised plagiarism, remix, or collage of the source data. On these grounds, many have argued for stronger regulations on the deployment, use, and attribution of the output of these models. However, these issues are not new or unique to artificial intelligence. In this position paper, using examples from literary criticism, the history of art, and copyright law, I show how creativity and originality resist definition as a notatable or information-theoretic property of an object, and instead can be seen as the property of a process, an author, or a viewer. Further alternative views hold that all creative work is essentially reuse (mostly without attribution), or that randomness itself can be creative. I suggest that creativity is ultimately defined by communities of creators and receivers, and the deemed sources of creativity in a workflow often depend on which parts of the workflow can be automated. Using examples from recent studies of AI in creative knowledge work, I suggest that AI shifts knowledge work from material production to critical integration. This position paper aims to begin a conversation around a more nuanced approach to the problems of creativity and credit assignment for generative models, one which more fully recognises the importance of the creative and curatorial voice of the users of these models and moves away from simpler notational or information-theoretic views.

翻译：人工智能（AI），尤其是生成模型，正在成为知识工作的变革性工具。它们对创造力、原创性、抄袭、归属认定及版权所有权等概念提出了挑战。批评者强调生成模型对大量训练数据的依赖，并将其输出视为对源数据的随机化抄袭、重混或拼贴。基于此，许多人主张对生成模型的部署、使用及其输出的归属实施更严格的监管。然而，这些问题并非人工智能所独有或全新。在本立场论文中，我借助文学批评、艺术史和版权法中的实例，展示创造力与原创性如何难以被定义为客体的可表示性或信息论属性，而是可以被视为过程、作者或观看者的属性。此外，另类观点认为，所有创造性工作本质上都是再利用（多数情况下不标注出处），或者随机性本身即可具有创造性。我认为，创造力最终由创作者与接收者社群定义，而工作流中被视为创造力来源的部分，通常取决于工作流中哪些环节可实现自动化。通过引用近期人工智能在创造性知识工作中的应用研究案例，我提出：人工智能将知识工作从物质生产转向批判性整合。本立场论文旨在开启一场关于生成模型创造力与归属问题的更细致探讨，这种探讨更充分地认识到模型使用者的创造性策展声音的重要性，并超越简单的表示性或信息论视角。