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.
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.
Drawing on the Beeck Center’s research on government, nonprofit, academic, and private sector organizations that are working to improve access to safety net benefits, this report highlights best practices for creating accessible benefits content.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
The article presents the True Cost of Economic Security (TCES) measure, showing that over half of U.S. families struggle to meet the comprehensive costs required to thrive, highlighting significant disparities based on family type, location, and race.
This playbook offers a comprehensive guide for designing, implementing, and evaluating a guaranteed income program specifically for individuals experiencing homelessness.
The Ethical Artificial Intelligence (AI) Policy of the City of Tempe establishes principles and governance structures to ensure the responsible, fair, and transparent use of AI in municipal operations.
The Long Beach Tree Map shows trees throughout the Long Beach region which centralizes, organizes, and visualizes information regarding where and how many trees as well as their type.
