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Data inequality and subpopulation shift are the two key challenges in multiethnic machine learning. These challenges are being addressed on two fronts. Collecting more ancestrally diverse data will gradually reduce the degree of data inequality, and algorithmic intervention (e.g., transfer learning) can mitigate the impacts of data inequality and subpopulation shift on multiethnic machine learning.
