Beyond the Prompt: Rethinking Authorship and Originality in AI-Assisted Graphic Design

Abstract

Only several years after their introduction, generative AI-based image generation tools like Midjourney, DALL-E, or Stable Diffusion became an integral component in the routine design process of graphic designers, raising anew an age-old question: who authors a graphic work, and what is its originality? This paper is a conceptual and critical synthesis of relevant literature rather than an empirical study. No original data collection was conducted here; the paper synthesizes legal cases, guidelines, scholarship produced by practitioners and in design education, and experimental research on generative model biases in order to trace how GenAI is altering the understanding of authorship and originality in graphic design through copyright law, professional practices, visual aesthetics, and design education. GenAI does not render human authorship irrelevant, as was claimed by many in the early discussion of the topic. Rather, it becomes relative depending on how specific the human input in terms of creative direction is, how deep the post-generation editing is and how effectively a designer is able to justify the design choices. Copyright law authorities in the US, UK, and EU reach the consensus on the principle of protecting only works created with demonstrable human authorial input; however, each one uses its own criteria to measure this input. According to practitioner research, the function of designers changes from the creator of visuals to curators and editors of the machine output. On the other hand, experimental research of generative models demonstrates homogenization of styles and representational bias inherited from training data, but also improvements in efficiency and positive evaluation by audiences of GenAI-assisted graphics. The field where these issues are addressed is design education. The paper concludes by using the existing control-based legal scholarship to develop a multi-dimensional framework which can be used by practitioners, educators, and reviewers to describe GenAI-assisted works.