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A growing body of work shows that many problems in fairness, accountability,transparency, and ethics in machine learning systems are rooted in decisionssurrounding the data collection and annotation process. In spite of itsfundamental nature however, data collection remains an overlooked part of themachine learning (ML) pipeline. In this paper, we argue that a newspecialization should be formed within ML that is focused on methodologies fordata collection and annotation: efforts that require institutional frameworksand procedures. Specifically for sociocultural data, parallels can be drawnfrom archives and libraries. Archives are the longest standing communal effortto gather human information and archive scholars have already developed thelanguage and procedures to address and discuss many challenges pertaining todata collection such as consent, power, inclusivity, transparency, and ethics &privacy. We discuss these five key approaches in document collection practicesin archives that can inform data collection in sociocultural ML. By showingdata collection practices from another field, we encourage ML research to bemore cognizant and systematic in data collection and draw frominterdisciplinary expertise.

Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning