Munging the Ghosts in the Machine: Coded Bias and the Craft of Wrangling Archival Data

Data wrangling is typically treated as an obligatory, codified, and ideally automated step in the machine learning (ML) pipeline. In contrast, we suggest that archival data wrangling is a theory-driven process best understood as a practical craft. Drawing on empirical examples from contemporary computational social science, we identify nine core modes of data wrangling, which can be seen as a sequence but are iterative and nonlinear in practice. Moreover, we discuss how data wrangling can address issues of algorithmic bias. While ML has shifted the focus towards architectural engineering, we assert that to properly engage with machine learning is to properly engage with data wrangling.