This paper discusses prominent examples of what we call “algorithmic anxiety” in artworks engaging with algorithms. In particular, we consider the ways in which artists such as Zach Blas, Adam Harvey and Sterling Crispin design artworks to consider and critique the algorithmic normativities that materialize in facial recognition technologies. Many of the artworks we consider center on the face, and use either camouflage technology or forms of masking to counter the surveillance effects of recognition technologies. Analyzing their works, we argue they on the one hand reiterate and reify a modernist conception of the self when they conjure and imagination of Big Brother surveillance. Yet on the other hand, their emphasis on masks and on camouflage also moves beyond such more conventional critiques of algorithmic normativities, and invites reflection on ways of relating to technology beyond the affirmation of the liberal, privacy-obsessed self. In this way, and in particular by foregrounding the relational modalities of the mask and of camouflage, we argue academic observers of algorithmic recognition technologies can find inspiration in artistic algorithmic imaginaries.

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Big Data and Society
Erasmus University Rotterdam

de Vries, P. (Patricia), & Schinkel, W. (2019). Algorithmic anxiety: Masks and camouflage in artistic imaginaries of facial recognition algorithms. Big Data and Society, 6(1). doi:10.1177/2053951719851532