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 talk will explore some of the technical, political, and institutional factors that lead to algorithmic harms and will introduce an agenda for developing and regulating algorithms in the interest of equity and social justice.