Core Post 3: Cultural Algorithms

This week’s readings with their explicit positioning in STS or Critical Information Studies definitely hit home for me, and actually address many of the fundamental queries that guide my current study on the convergence of music streaming services and mobile dating platforms. The fictions of mechanical neutrality and “culture-less” science have come to occupy a central position in my research, and unearthing the cultural, social, economic, and political embedded in technological infrastructures that tout rationality and objectivity constitutes much of my current work on Spotify algorithms and their capacity to regulate user behavior and self-concept. But to focus specifically on the four readings for this week, all of the authors, in one way or another, bring to our attention the crystallization and proliferation of hegemonic discourses through the performance of algorithmic objectivity. Considering, here, Gillepsie’s discussion of the “recursive loop” (p. 17) in which he points out that the scope of the algorithm is essentially limited to the archive to which it is applied, I wonder if the algorithmic biases that Noble lays bare are, to a certain extent, not entirely algorithmic biases, but rather somewhat reflective of actually existing available information or “cultural algorithms” (p. 25) that are products of larger regimes of structural oppression. To elaborate, I am wondering to what extent is the reproduction of racist and sexist discourses online the product of existing problematic cultural frameworks being reflected in the algorithmic architecture compared to the actual negligence of corporations in reproducing them. To what extent is it inevitable vs. ‘designful’? Bleakly put, can algorithms ever exist outside the agendas of White masculine hegemony, neoliberalism, and capitalism, all of which are also tightly bound together themselves? I’m thinking here of Gillepsie’s example:

… a search for the phrase, “she invented,” would return the query, “did you mean ‘he invented’?” While unsettling in its gender politics, Google’s response was completely “correct,” explained by the sorry fact that over the entire corpus of the web, the word “invented” is preceded by “he” much more often than “she.” Google recognized this – and mistakenly presumed it meant the search query “she invented” was merely a typographical error. Google, here, proves much less sexist than we are. (p. 25)

Considering that Noble points out that Google has the capacity to “tweak” or “fix” problematic search returns when they are exposed (p. 82), perhaps what is desirable, then, is not the mechanical neutrality that algorithmic applications tout, but rather, explicitly ‘partial’ ethical algorithms that mobilize their totalizing capacity to actively counter the oppressive social and cultural discourses they currently perpetuate…