Intersectionality and reflexivity-decolonizing methodologies for the data science process.

Abstract

Using intersectionality as a methodology illuminated the shortcomings of the data science process when analyzing the viral #metoo movement and simultaneously allowed me to reflect on my role in that process. The key is to implement intersectionality to its fullest potential, to expose nuances and inequities, alter our approaches from the standard perfunctory tasks, reflect how we aid and abide by systems and structures of power, and begin to break the habit of recolonizing ourselves as data scientists.