Paper short abstract:

What can we (un)learn from adopting the oppressive gaze of algorithmic violence? This reflection will take shape as a live video-essay, exploring practice-based work that uses algorithmic surveillance images to question algorithmic surveillance itself.

Paper long abstract:

Computer vision algorithms both emerge from and give support to surveillance infrastructures: on one hand these systems are most often born out of the data scraped from the open web, organized as data sets (cf. Denton et al., 2021); on the other hand, these algorithms are becoming the backbone of algorithmic surveillance in supermarkets, streets, and war zones (cf. Bellanova et al., 2021).

In parallel, much STS scholarship has aimed at making visible the digital infrastructures that undergird our everyday life (e.g. Parks, 2015; Blanchette, 2012). Practice-based research has questioned the assumptions embedded in infrastructures, ranging from "datawalking" the smart city (van Es & de Lange, 2020) to drawing an anatomy of AI (Crawford & Joler, 2019).

Building on these points, my question is: How may we conceptualize practice-based engagement with computer vision as a surveillance infrastructure? How may we engage with algorithmic surveillance itself as a way for questioning algorithmic surveillance? What conceptual and ethical issues are born from seeing like algorithmic surveillance? Finally, what can we (un)learn from adopting the oppressive gaze of algorithmic violence?

This reflection will take shape as a live video-essay (a talk with short video segments). I will reference art/scholarship on STS/surveillance which uses the surveillance apparatus, such as: Theo Antony's movie "All Light, Everywhere" (2021), made with police body cams; and Manu Luksch's "Faceless" (2007), entirely made using London's CCTV. In parallel, I will reflect on my in-progress work on the colonial history of Automated License Plate Recognition.