Designing Attribution: Technical Disclosure in AI-Mediated Art

Generative AI challenges established notions of authorship in contemporary art, yet little attention has been paid to how technical aspects of AI systems are disclosed in exhibition contexts. This paper presents a qualitative analysis of 23 AI-mediated artworks, with a focused discussion of 13 cases selected to illustrate distinct disclosure practices. We examine how model architectures, data provenance, interaction modalities, and energy consumption are framed or omitted in attribution materials. Our analysis reveals recurring asymmetries: model architectures are frequently named but rarely explained, data are disclosed selectively, and energy use is systematically absent. Instead of framing technical disclosure as an obligation, we approach it as a designed practice shaped by the artist. Three disclosure strategies emerge: extended technical framing, symbolic technical naming, and strategic opacity. In a climate of growing skepticism toward AI-generated outputs, technical attribution is not neutral reporting, but an active site where legitimacy and authorship are constructed.