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

Research output: Contribution to journalArticleProfessional


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.
Original languageEnglish
Pages (from-to)235-261
Number of pages27
Issue number2
Publication statusPublished - 23 May 2023
MoE publication typeNot Eligible


Dive into the research topics of 'Living with images from large-scale data sets: A critical pedagogy for scaling down'. Together they form a unique fingerprint.

Cite this