Governments increasingly use algorithms (such as machine learning predictions) as a central tool to distribute resources and make important decisions. Although these algorithms are often hailed for their ability to improve public policy implementation, they also raise significant concerns related to racial oppression, surveillance, inequality, technocracy, and privatization. While some government algorithms demonstrate an ability to advance important public policy goals, others—such as predictive policing, facial recognition, and welfare fraud detection—exacerbate already unjust policies and institutions. This class examines the opportunities and challenges raised by the use of algorithms in public policy. The course aims to help students 1) build the interdisciplinary skills to thoughtfully reason about the social benefits and harms of government algorithms, 2) apply these frameworks to study policy domains where algorithms are being applied and debated, and 3) analyze and develop policy interventions for regulating the use of algorithms in government. Throughout, we will look to both theoretical writings regarding the politics of algorithms and applied case studies of computational systems and public policies in practice. Students will engage with these topics through readings, class discussions, and a research paper related to the topics of this course. No prior technical background is necessary.