Toward a Minor Tech:Crichlow
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Scaling Up, Scaling Down: Racialism in the Age of ‘Big Data’
The story of race, as Paul Gilroy tells it, moves simultaneously inwards and downwards. Breaking the surface of skin and enveloping the racial body politic in ever-minute scales of perceptual closeness, the genomic revolution of the 1990’s gestured toward racialism’s still potential demise: the end of race itself. As older conceptions of race were belied by breakthroughs in molecular biology, the representational regimes to which racialism was attached were ambivalently undone. In this movement beyond the old visual signifiers of race, Gilroy notes how the human body ceases to “delimit the scale upon which assessments of the unity and variation of the species are to be made” (845). In more contemporary terms, however, there is a sense that race is being remade not within extant contours of the body’s visibility, but outside corporeal recognition altogether. What if scalar compression of the microscopic to the molecular —a movement of race-craft inwards and downwards— now exerts upwards and outwards pressures into a globalised regime of datafication? To extend Gilroy differently in the present context of algorithmic culture, I consider how racial epistemology is reproduced, reconstructed, and reified within the scalar magnitude of ‘big data’. In other words, I want to complicate the stakes and possibilities for dismantling racialism when the body is no longer its primary referent.
As large-scale automated data processing reproduces patterns of racialisation indiscernible to the human eye, the question of scale has again become relevant to a post-visual discourse of race. Following Joshua DiCaglio, I evoke scale here as an integral mechanism of observation that establishes “a reference point for domains of experience and interaction” (2021, p. 3). Scale structures the relationship between the body and its abstract signifiers, between identity and its lived outcomes. Race has always been a technology of scale: a tool to define the minute, miniscule, microscopic signifiers of a human ideal against an imagined nonhuman ‘other’. Rather than assume the truth of racial identity in an imagined “mute body”, analytic surveillance technologies produce racialisation as a scalar function of mass swathes of processed data (Gilroy 1998, p. 847). Predictive policing, for example, increasingly relies on an accumulation of data to construct zones of suspicion through which the racial body is interrogated (Brayne 2020; Chun 2021). ‘Suspicious’ (code word: Muslim) subjects flagged by the theatre of algorithmic security systems are rendered immobile at the border (Amoore 2006). Automated welfare eligibility checks keep struggling people from accessing the resources to which they are entitled (Rao 2019; Toos 2021). Credit-market algorithms widen the racialised gap between the haves and the have nots (Bhutta et. al 2022). While racial categories are not explicitly coded within the classificatory techniques of analytic technologies, large-scale automated data processing condense and map racialising outputs that appear neutral. Thao Than and Scott Wark define these algorithmically generated racial formations as ‘data formations’: that is, “modes of classification that operate through proxies and abstractions and that figure racialized bodies not as single, coherent subjects, but as shifting clusters of data” (1, 2020).
Gilroy too predicted a shift that would constitute race as an entity divorced from perceptual regimes of the human eye. But rather than moving inwards, towards the invisible genomic interfaces of the body, algorithmic processes of classification constitute a digital re-coding of race by proxy en mass. This is not to say that racialism as it has been historically constituted is being dismantled by the grand scale of computational processing; or that other modes of racialist discourse are not still firmly rooted within material experience. Rather, I reference the loosening of race from the grips of not only ocular modes of seeing, but perceptual regimes of racial scale, whereby race category is not only assigned to the small-scall signifiers of the body, but inferred through large-scale algorithmic correlation, categorisation, and abstraction of data. While racialisation and data have always been constitutive (Womack 2021; Zuberi 2001), the scale of ‘big data’ mask an insidious realignment whereby race seems to disappear, while its effects are more deeply inscribed within lived experience.
So, are we approaching a time where the age of visual, or embodied conceptions of racialism are ending? Not so fast – I would like to complicate this a bit further. Biometric technologies that produce digitised templates of bodily characteristics for authentication or verification purposes have troubled the notion that we have left behind racialism’s sticky attachment to the minute, perceptual scales of bodily difference in the digital age. Scholars such as Joseph Pugliese have shown how biometric technologies are ‘infrastructurally calibrated to whiteness’ in their reduced capacity to recognize dark-skinned faces (2012, p. 57). In this regard, biometric technologies relegate racialised bodies outside the scope of human recognition, while at the same time, disproportionately subjecting them to heightened surveillance in service of local and global security apparatuses. What this disparity demonstrates, is that while forms of racialisation are increasingly migrating to the terrain of the digital, the epidermal materialisation of race has not yet faded, but is experiencing a resurgence in new digitised forms. Biometric technologies fit under the umbrella of ‘big data’ given that they often process large volumes of data and analytics. And yet, their capacity to racialise and produce difference is directly tied to the body – to the ‘material’ site of race itself. These extant tensions between data and the lived, phenotypic, or embodied constitution of racialism suggests that these two racialising formats interlink and reinforce each other.
The emergence of digital technologies and ‘big data’ may not, as Gilroy imagined, result in the ‘end’ of race. Rather, these technologies have complicated it. As racialism migrates to post-visual registers of datafication, residual modes of racialization remain intact in biometric modes of imaging the body. Yet, racialisation is not overdetermined by large-scale automated data processing. Beyond ‘opting out’ of data regimes or obfuscating oneself from surveillance apparatuses, possibilities of transfiguration, and transformation that refuse racialising and colonialist ‘data relations’ remain conceivable (Couldry and Mejias). This begins with refusing the absolute universality and totality that ‘big data’ regimes attempt to guarantee under the pretense of neutrality. Initiatives such as the The Distributed Artificial Intelligence Research Institute, for example, use data to examine the effects of discriminatory policies, most recently publishing a case study on spatial apartheid in South Africa (Gebru et al. 2021). This study points to the potential capabilities of large-scale data analysis to redress the historical effects of racialisation. Here, big data analytics do not reconstruct racial category, but may be mobilised towards the liberatory practices that reframe ‘big data’ and transform domains of experience toward an end of race futurity.
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