Marisol Vasquez Core Response 2 / 5

"From the beginning, then, algorithms have encoded a particular kind of abstraction, the abstraction of the desire for an answer" (Finn 25). 

I open with this quote from Finn's "What is an Algorithm" to raise a question regarding data collection and the archive. First, to unpack Finn's statement, I want to examine what is implicated by the desire for an answer. As Finn claims, desire is encoded into a pursuit of effectiveness that obscures within computational regimes. (Finn 26) If we were to define this "desire" in the algorithm, according to Finn and the evidence the author lays forth, it might have its origins in what N. Katherine Hayles refers to as the 'hard claim' for computationalism, in which the cultural processes "are themselves computational machines that can be mathematically duplicated" (22). As Finn elegantly points to, algorithms might be built, produced, and constructed around this desire for accuracy, which manifests as simulations of “election outcomes,” “future price stocks,” or—as exemplified in Juan De Lara’s “Race, Algorithms, and the Work of Border Enforcement” and Sofiya Umoja Noble’s Algorithms of Oppression—a desire for accuracy can also figure through the production and reproduction of racialized bodies. 

As the production and reproduction of racialized bodies implicates, this desire for accuracy is laced with ideological drawbacks, such as the reproduction of the US racial state, as De Lara elucidates. Computational processes—their seductive allure of accuracy and effectiveness—obscure the consequences of blinded algorithmic production. As Finn claims, “universal computation” and computational models for cultural problems lay forth serious risks and downfalls in which computational models can “compress or shorthand reality” (23). This ostensible eclipse of “reality” might be considered in their national contexts, as exemplified by De Lara specifically, who dismantles the classification of a ‘risky body’ as it tethers to an imaginary of the US racial state. As De Lara exemplifies, the case of the U.S. racial state and the genealogies of America’s nativist politics (154) or epistemologies of anti-immigration (154) constitute the desire embedded within the production of the algorithm and its accompanying border control technologies. I found it curious how academic research and Silicon Valley partook in developing earlier technologies of the virtual border, which led me to question who is ultimately the arbiter of the production of desire within algorithms? This is a similar question to Stone’s essay and her case study of VR engineers. However, with algorithmic production, De Lara’s and Noble’s work seeks to dismantle the ideological underpinnings that inform such a reproduction of desire through the computation of a racialized body.   

In the work of cultural studies, however, I would also like to ‘imagine otherwise,’ while De Lara and Noble deployed race to disassemble the power imbalances and racial hierarchy assumed within computational processes, what other forms of cultural production via computation have people produced that seek to imagine otherwise? Coming from a film studies background, I raise this question frequently, and I also believe it ties into Kara Keeling’s work and the concept of “Queer OS” to queer or reshape computation’s dominant logic of desire, and the reproduction of race, gender, class, citizenship, and ability within a nation-state. To offer another concluding point, this work led me to think about the colonial archive and the racial taxonomies produced upon “the emergence of European liberalism, settler colonialism in the Americas, the transatlantic African slave trade, and the East Indies and China trades in the late eighteenth and early nineteenth centuries” (Lowe, 1). In other words, I point to the colonial archive to think about how computation, algorithms, data collection, and archive theory hearken back to and index the construction of racial taxonomies and share a relation under the dominant logic of ‘Western science’ that seem to suffuse through many of these critiques on algorithmic production.