Creative signatures and systemic bias: A critical analysis of platform affordances in generative diffusion models

The emergence of text-to-image AI platforms (Flux, Midjourney, and Stable Diffusion) represents a profound shift in creative technology, yet a critical sociotechnical understanding of how proprietary architectures encode underlying sociopolitical biases remains fundamentally underdeveloped. This study utilizes the Collaborative AI Creativity Model, a novel framework for integrated social-technical evaluation across 306 generated images. Rigorous analysis reveals platform-specific interpretative “creative signatures”: Flux affords stability and utility through high technical precision and prompt adherence, while Midjourney affords aesthetic agency via metaphorical interpretation and creative expansion. Critically, the research documents a systemic divergence in subject matter handling: non-social scenes (e.g., Nature/Landscape) achieved high success, contrasting sharply with a structural failure in rendering complex Human Group Scenes (success rates dropping significantly to 18.8%–31.2%). This complexity-dependent performance degradation is interpreted not as technical error but as empirical evidence of systemic algorithmic defaults and inherent biases rooted in skewed training data. It confirms an algorithmic worldview that inherently prioritizes the processing of easily quantifiable material culture over nuanced social representation. These results, analysed through Affordance Theory, necessitate an urgent critical shift toward understanding generative AI through the prism of political economy, demanding adversarial design methodologies to mitigate embedded systemic bias in AI images.