Resisting epistemic loss in AI image generation.
Where this comes from
- Record sourced from PubMed, PMID 42328197.
- Also identified by DOI 10.1016/j.patter.2026.101568 and PMC identifier 13280676.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Hintze et al.'s recent study highlights the tendency of current-generation vision-language models to converge on overly generic outputs. We argue that considering AI imageries as epistemic artefact and AI-driven artistic practices as socio-cultural processes can provide better understandings around the implications of such conditions beyond merely technical aspects of image generation. Drawing from our ongoing research, we highlight the necessity of bringing the epistemic vulnerability of marginalized artists and users and their hierarchical relations with these tools into sociotechnical design conversations, and by doing so, to explore the possibility for pluriversal and just AI futures, particularly in the Global South.