Curating in the Wild: Taming the Indeterminacy of the Networked Image

Abstract

This chapter examines the emergence of machine vision and computational aesthetics as new sites of curatorial practice in the networked image economy. As photography circulates at planetary scale, images exist simultaneously as visual objects and as data, producing a “semantic gap” between human and machine interpretation. We argue that computer scientists, engineers, and algorithmic systems have become significant - yet largely unacknowledged - curatorial agents, tasked with stabilising the indeterminacy of networked images through dataset construction, annotation, and algorithmic filtering. Through the concept of “curating in the wild,” we describe a paradoxical condition in which curation operates both to train algorithms and as a function performed by them, enabling the extraction of aesthetic, cultural, and economic value from ubiquitous photographic production. Drawing on the example of the EyeEm platform, we show how curatorial discourse is mobilised to frame computational selection as care, while masking the infrastructural and economic logics of data extraction. Ultimately, we argue that curation has migrated from the museum to the computational pipeline, where it serves to render networked images tractable to machine perception and valorisation, reshaping the politics, aesthetics, and governance of contemporary visual culture. Show more