School Choice Algorithms: Data Infrastructures, Automation, and Inequality

Abstract

Automated decision-making is a process in which an algorithm collects and analyses data, derives information, applies this information, and recommends an action, at times using forms of Artificial Intelligence (Richardson 2021). This paper proposes that we need to locate automated decision-making as part of the history of educational policy and governance, as well as increasingly networked cultural records or digital archives. As such, we explore the history and present of automated decision systems across a range of cultural records spanning several categories: data, algorithm, and AI-based technologies; innovation and industry; philanthropy and funding; policy and legislation; spatiality and socioeconomics; plus, activism, and communities. To do so, we created an interdisciplinary archival heuristic as a research tool to retrace these interrelated cultural records shaping data infrastructure and inequalities. We then tested this tool in the context of the school admission matching algorithm in New York City. Our central aim is to help counter discourses about the newness and efficiencies of introducing automation and algorithms across education reform initiatives. The education counter-archiving heuristic introduced therefore offers a novel research tool to explore the intersecting history, present, and future of automated decision-making systems, such as school choice algorithms.