It's a Feature, Not a Bug: Measuring Creative Fluidity in Image Generators
This addresses the debate on AI creativity for researchers and artists, though it is incremental in providing a new metric rather than a breakthrough.
The paper tackles the problem of measuring creativity in AI image generators by defining and quantifying 'fluidity of prompt interpretation' through experiments on popular models, finding that some generators exhibit significant fluidity as measured by breakage points in auto-generated chains.
With the rise of freely available image generators, AI-generated art has become the centre of a series of heated debates, one of which concerns the concept of human creativity. Can an image generation AI exhibit ``creativity'' of the same type that artists do, and if so, how does that manifest? Our paper attempts to define and empirically measure one facet of creative behavior in AI, by conducting an experiment to quantify the "fluidity of prompt interpretation", or just "fluidity", in a series of selected popular image generators. To study fluidity, we (1) introduce a clear definition for it, (2) create chains of auto-generated prompts and images seeded with an initial "ground-truth: image, (3) measure these chains' breakage points using preexisting visual and semantic metrics, and (4) use both statistical tests and visual explanations to study these chains and determine whether the image generators used to produce them exhibit significant fluidity.