\u201cBig Data\u201d and \u201cartificial intelligence\u201d have captured the public imagination and are profoundly shaping social, economic, and political spheres. Through an interrogation of the histories, perceptions, and practices that shape these technologies, we problematize the myths that animate the supposed \u201cmagic\u201d of these systems. In the face of an increasingly widespread blind faith in data-driven technologies, we argue for grounding machine learning-based practices and untethering them from hype and fear cycles. One path forward is to develop a rich methodological framework for addressing the strengths and weaknesses of doing data analysis. Through provocatively reimagining machine learning as computational ethnography, we invite practitioners to prioritize methodological reflection and recognize that all knowledge work is situated practice.<\/p>\n","protected":false},"excerpt":{"rendered":"
\u201cBig Data\u201d and \u201cartificial intelligence\u201d have captured the public imagination and are profoundly shaping social, economic, and political spheres. Through an interrogation of the histories, perceptions, and practices that shape these technologies, we problematize the myths that animate the supposed \u201cmagic\u201d of these systems. In the face of an increasingly widespread blind faith in data-driven 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