The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
This article examines how administrative burdens in U.S. social safety net programs have changed over the past 30 years, showing that while average burdens have declined, inequality in who faces these burdens has grown.
The ANNALS of the American Academy of Political and Social Science
This academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This study examines how providing information about administrative burden influences public support for government programs like TANF, showing that awareness of these burdens can increase favorability toward the programs and their recipients.
This research summary presents findings from a randomized controlled trial demonstrating how mRelief’s simplified SNAP application significantly increases application rates among eligible individuals.
This article examines the concept of "viral cash" and suggests that the future growth of basic income programs will depend on advocacy networks rather than traditional policy diffusion across jurisdictions.
This paper explores how legacy procurement processes in U.S. cities shape the acquisition and governance of AI tools, based on interviews with local government employees.
This article explores how anticipatory logics—drawing from foresight, futures thinking, and design—are shaping the future of government by creating space for innovative policy approaches, public participation, and proactive governance.
