This brief outlines the U.S. federal government’s framework to identify, reduce, and address administrative burdens through a series of executive orders, legislative actions, and updated policies focused on improving customer experience and increasing access to government benefits.
This report explores the financial challenges faced by U.S. workers, analyzing the roles of work arrangements and public and workplace benefits in achieving financial security, while highlighting the disparities in access and effectiveness for low- and moderate-income workers.
The report examines how current remote identity proofing methods can create barriers to Medicaid enrollment and suggests improvements to ensure equitable access for all applicants.
Annual Computers, Software, and Applications Conference (COMPSAC)
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
This brief describes the TANF Data Collaborative (TDC), an innovative approach to increasing data analytics capacity at state Temporary Assistance for Needy Families (TANF) agencies.
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
A webinar presenting fresh data on how young adults aged 22 are faring in terms of poverty, employment, education, living arrangements, and access to public benefits.
This report poses the question of whether states are prepared to meet the new Medicaid work reporting and renewal mandates introduced by HR 1, given ongoing strain from the post-pandemic “unwinding.”
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.
