This review looks at two technical papers from the field of computer science that, at the time of writing, should be considered historical. Although their respective technical approaches have since been replaced with newer, better, and more efficient ones, when looking back through the lens of critical AI studies they mark the beginning of a type of theoretical reflection within computer science that distinctly links technical machine learning research to research in the humanities.
Machine learning models are cultural artifacts. They are trained on (limited) real-world data and often designed to make decisions with real-world impacts. The relation of a machine learning model to the world is thus a relation of interest. What kind of representations do machine learning models produce? As the world is necessarily mirrored in a machine learning system to some degree, as there exists, with Walter Benjamin, an approximation of a mimetic faculty, what are the...
