Living with images from large-scale data sets: A critical pedagogy for scaling down

The emergence of contemporary computer vision coincides with the growth and dissemination of large-scale image data sets. The grandeur of such image collections has raised fascination and concern. This article critically interrogates the assumption of scale in computer vision by asking: What can be gained by scaling down and living with images from large-scale data sets? We present results from a practice-based methodology: an ongoing exchange of individual images from data sets with selected participants. The results of this empirical inquiry help to consider how a durational engagement with such images elicits profound and variously situated meanings beyond the apparent visual content used by algorithms. We adopt the lens of critical pedagogy to untangle the role of data sets in teaching and learning, thus raising two discussion points: First, regarding how the focus on scale ignores the complexity and situatedness of images, and what it would mean for algorithms to embed more reflexive ways of seeing; Second, concerning how scaling down may support a critical literacy around data sets, raising critical consciousness around computer vision. To support the dissemination of this practice and the critical development of algorithms, we have produced a teaching plan and a tool for classroom use.