Compulsion beyond fairness: towards a critical theory of technological abstraction in neural networks

In the field of applied computer research, the problem of the reinforcement of existing inequalities through the processing of “big data” in neural networks is typically addressed via concepts of representation and fairness. These approaches, however, tend to overlook the limits of the liberal antidiscrimination discourse, which are well established in critical theory. In this paper, I address these limits and propose a different framework for understanding technologically amplified oppression departing from the notion of “mute compulsion” (Marx), a specifically modern form of power based on the social abstraction of wage labor in market exchange. I begin with a reconstruction of the process of technological abstraction in automated pattern recognition, after which I summarize the conceptional shortcomings of representational fairness discourse. In contrast, I demonstrate how the social reproduction of exploitation is conceptualized in critical theory as being mediated through a process of social abstraction, defined as a social practice of reification that manifest in fetishization. To avoid economistic shortcomings, I contextualize the argument of social abstraction against the backdrop of social reproduction theory, which takes the juncture of various historical forms of oppression into account. I conclude by elaborating how technological abstraction corresponds to the problem of mute compulsion by replicating processes of social abstraction.