This article explores how integrating behavioral science into public administration can improve government effectiveness, equity, and trust by redesigning public services with human behavior in mind.
A national survey of low-wage workers showing that administrative burdens in SNAP and Medicaid are common and strongly linked to food hardship, healthcare hardship, and chronic illness.
This research paper examines how stigma shapes participation in U.S. social safety net programs and influences public support for benefit design and access.
This research article explores how framing income eligibility guidelines in either dollar amounts or as a percentage of the Federal Poverty Line (FPL) affects public attitudes toward program access and administrative burdens in Medicaid and SNAP.
A TLDR of the State CDO Archetypes report—covering how state CDO offices operate and the six archetypes that define them. Written for event attendees and government staff: governor's office, IT and budget leadership, legal and data officials, and legislators who oversee CDO funding and establishment.
This article analyzes the translation of law into computer code and the use of automated decision-making systems in government to make legal distinctions. Specifically, how are algorithmic decisions tied to law, and what happens when legal effects are mediated through technologies?
This resource is a research paper examining the role of the public safety net in insuring job losers against income loss, analyzing which government programs provide financial support and how benefits vary based on pre-job loss income levels.
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.
This paper describes results from fieldwork conducted at a social services site where the workers evaluate citizens' applications for food and medical assistance submitted via an e-government system. These results suggest value tensions that result - not from different stakeholders with different values - but from differences among how stakeholders enact the same shared value in practice.
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
This study assesses five commercial RIdV solutions for equity across demographic groups and finds that two are equitable, while two have inequitable performance for certain demographics.
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.
