  This article makes two key contributions to methodological debates in
automation research. First, we argue for and demonstrate how methods in this
field must account for intersections of social difference, such as race, class,
ethnicity, culture, and disability, in more nuanced ways. Second, we consider
the complexities of bringing together computational and qualitative methods in
an intersectional methodological approach while also arguing that in their
respective subjects (machines and human subjects) and conceptual scope they
enable a specific dialogue on intersectionality and automation to be
articulated. We draw on field reflections from a project that combines an
analysis of intersectional bias in language models with findings from a
community workshop on the frustrations and aspirations produced through
engagement with everyday AI-driven technologies in the context of care.
