Scrutability of the “black box”: machine learning & social justice in educational measurement

Canada relies heavily on the role of language proficiency tests in determining suitability for high-stakes decisions such as admission to study programs, employment, residency, and citizenship. Increasingly, such tests are administered by automated scoring systems, employing machine learning models, which have been criticized for “black box” elements leading to inscrutable and unexplainable models of assessment.