Black Futures: Collecting Sociocultural Data through Machine Learning

Although African American community archives have appeared, there is a lack of incorporation of information-seeking, behaviour, language transmission, categorization, and community archival datasets in data collection and machine learning (ML) environments. To address this, as the autoethnographer, I propose to develop a future body of research targeting the African American community in Texas, obtaining valuable insights about their engagement with ML. Eun Seo Jo's literature review emphasizes the roles of community archives in ML environments and the strategies necessary for this space to be considered a valuable resource in research and information. As the auto-ethnographer, I use this research to explore effective strategies for machine learning environments to collaborate with African American community archives and incorporate user input into ML data collection practices. The aim of the study is to examine an original body of literature to aid me with my plan of action in creating a research study about machine learning in African American community archives.