Paper short abstract:

In this paper I ask how the objects of machine intelligence reveal the “dialogicality” of the social worlds from which they are drawn. I develop a method to subject these objects to ethnographic scrutiny, to reveal the means through which machine intelligence constructs its objects of knowledge.

Paper long abstract:

In this paper I ask how the objects of machine intelligence reveal, upon ethnographic scrutiny, the “dialogicality” of the social worlds from which they are drawn. I subject these objects to scrutiny, as a method, to reveal the means through which machine intelligence constructs its objects of knowledge. I treat the process of producing objects of knowledge through machine intelligence as a “text-artifact” which applied machine learning researchers “decontextualize” as they produce knowledge. They transform—I argue—the text-artifact of data, algorithms, classifications, and predictions into purely “denotational” objects while eliding how the text-artifact “was originally laid down, or sedimented, in the course of a social process” (Silverstein and Urban 1996, 5). Social material-discursive practices are entextualized through the data collection and processing practices of machine intelligence. They are taken to stand in “for” the phenomena they purport to represent, and are “reanimated” through data performances that shift the ontological claims of that which is performed. This process is particularly evident when applied to natural language processing techniques, as I demonstrate in this paper, but I also propose that this process also applies to non-linguistic applications of machine intelligence.