  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.
