Algorithmic anxiety: Masks and camouflage in artistic imaginaries of facial recognition algorithms

Abstract

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, privacyobsessed 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.

A Pharmacological Perspective on Technology-Induced Organised Immaturity: The Care-giving Role of the Arts

“…As a subgenre of socially engaged art practices, anti-surveillance art initiatives have proliferated exponentially in recent decades, in line with the insidious and largely imperceptible invasion of facial-recognition technologies into almost all contemporary public spaces, from international airports and metro stations to city squares and local parks (Monahan, 2015). Anti-surveillance art encompasses a broad range of initiatives that directly engage with the “algorithmic anxieties” arising from the toxic normativities and “care-lessness” of facial-recognition technologies (de Vries & Schinkel, 2019). Anti-surveillance art provides practical creative solutions to disrupt and resist facial-recognition algorithms as a struggle against technology-induced anxieties (poisons), for example, systemic stupidity as well as racial injustice, gender discrimination, and loss of privacy immanent in the technological reduction of humans to tradable data traces (dividuation).…”

The oppositional affordances of data activism

“…This way, it challenges the imposed need for constant self-branding and gaining popularity. Twitter is being transformed from a tool enabling its established perceived affordances to a tool serving its own logic, namely affording critical and self-reflexive identity play and responding to the need for a ‘multiple, composite self’ (Van Dijck, 2013: 200), ‘a diffusion of subjectivity that opens up multiple ways of being’ (De Vries and Schinkel, 2019: 11). This unintended use is also an undesired use: users spending their time on Twitter observing other users’ activity and engaging in identity play, do not release useful behavioral data that would feed the business model of the platform (Hearn, 2017).…”

“…A hybrid approach (utilizing matter and technologies of facial analysis) is opted for by Zach Blas and his amorphous masks ( Facial Weaponization Suite ) that resist face recognition systems and at the same time raise issues pertaining to gender, race and sexual identities. This anti-affordance offers ‘the possibility to equip the face with a way of opting out and escaping from the logic of the visible’ (de Vries and Schinkel, 2019: 9; see also https://zachblas.info/works/facial-weaponization-suite/). Sterling Crispin's Data Masks are 3D printed face masks that are a result of reverse engineering facial recognition technologies, representing how human faces are seen by algorithms.…”

“Bodies not templates”: Contesting dominant algorithmic imaginaries

“…In recent years, obfuscation and camouflage-related projects such as CV Dazzle have become a recurring locus of intervention within different kinds of projects aiming to resist data-driven surveillance (Monahan, 2015). Artists and activists have played with makeup tutorials (Mayer, 2013), organized “Facial Recognition Defense Workshops” (Lewis, 2013), and created spin-off applications (Face Dazzler, n.d.) indicative of what De Vries and Schinkel (2019) understand as a manifestation of “algorithmic anxiety” over the looming threats of ubiquitous face surveillance—which might portend a broader “anti-facial recognition movement” (Cox, 2014).…”