Cássia Hosni, Giselle Beiguelman, Meta-Acervos and New Interpretations Emerging from Error
This visual essay examines the application of image classification and object detection algorithms to collections of drawings and paintings from Brazilian museums using Meta-Acervos, a platform that employs artificial intelligence models to analyze, organize and visualize artworks according to institutional, technical, chronological and visual descriptors. Rather than emphasizing the system’s accuracy, the essay focuses on its errors and on the potential for new readings afforded by ambiguity and interpretations not predicted by the program. We argue that this act of playing with errors and information not predicted within the logic of artificial intelligence models points to broader questions of agency and human intention in a world increasingly structured by apparatuses.
Absent Intelligence
This paper investigates how contemporary AI systems reproduce and amplify colonial hierarchies of knowledge. Through practice-led artistic research, Nouf Aljowaysir traces how the historical and digital visual record of the Arab world, largely produced by imperial actors, has been absorbed into AI training data, shaping both archival memory and algorithmic interpretation. In response, she developed Salaf (Ancestors), which uses AI segmentation techniques to remove orientalist figures from the Ken and Jenny Jacobson Orientalist Photography Collection at the Getty Museum, producing what she describes as an absent dataset. Drawing on Vilém Flusser's apparatus theory, the work argues that intelligence is defined by what has been narrowly recorded, digitized, and made legible online. By centering oral storytelling traditions and creating absent datasets, Aljowaysir's practice reveals how non-Western knowledge systems remain systematically marginalized within computational infrastructures that present themselves as universal.