Learning Under Algorithmic Conditions with Elizabeth de Freitas, Matthew X. Curinga, and Ezekiel J. Dixon-Román (U Minnesota Press, 2026)
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Learning Under Algorithmic Conditions with Elizabeth de Freitas, Matthew X. Curinga, and Ezekiel J. Dixon-Román (U Minnesota Press, 2026)
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What does it mean to learn when algorithms increasingly shape how knowledge is produced, organized, and interpreted? Editors Elizabeth de Freitas, Matthew X. Curinga, and Ezekiel J. Dixon-Román join Shreya Urvashi to talk about their book Learning Under Algorithmic Conditions (U Minnesota Press, 2026), a collection that examines how artificial intelligence and computational systems are changing education, cognition, and ideas of human intelligence. The editors discuss the complexities of algorithm, training data as curriculum, AI bias, and the concept of more-than-human learning. They also explore the relationship between contemporary technologies and colonial forms of reason, the limits of treating bias as a purely technical problem, and what happens to human agency as learning becomes increasingly entangled with algorithmic systems.
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