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Often framed as a relatively new and controversial phenomenon, the application of machine learning (ML) techniques to legal analysis and decision-making in the US justice system has a rich yet under examined history. My research examines how ML came to be adopted as a standard tool for automating fact discovery for high-stakes civil litigation. By analyzing the key controversies and consensuses that emerge during the experimentation and early adoption phase of this technology (2008-2015), a useful case study presents itself in which an expert professional field wrestled with the challenges of integrating ML into sensitive decision-making workflows.