Classifying humans: the indirect reverse operativity of machine vision

Abstract

Classifying is human. Classifying is also what machine vision technologies do. This article analyses the cybernetic loop between human and machine classification by examining artworks that depict instances of bias when machine vision is classifying humans and when humans classify visual datasets for machines. I propose the term ‘indirect reverse operativity’ – a concept built upon Ingrid Hoelzl’s and Remi Marie’s notion of ‘reverse operativity’ – to describe how classifying humans and machine classifiers operate in cybernetic information loops. Indirect reverse operativity is illustrated through two projects I have co-created: the Database of Machine Vision in Art, Games and Narrative and the artwork Suspicious Behavior. Through ‘artistic audits’ of selected artworks, a data analysis of how classification is represented in 500 creative works, and a reflection on my own artistic research in the Suspicious Behavior project, this article confronts and complicates assumptions of when and how bias is introduced into and propagates through machine vision classifiers. By examining cultural conceptions of machine vision bias which exemplify how humans operate machines and how machines operate humans through images, this article contributes fresh perspectives to the emerging field of critical dataset studies.