NYC's My File NYC and New Jersey's unemployment insurance system improvements demonstrate how successful digital innovations can be scaled across various programs, leveraging trust-building, open-source technology, and strategic partnerships.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
This quarterly research update aims to highlight key learnings related to improving unemployment insurance (UI) systems in the areas of equity, timeliness, and fraud, and monitor for model UI legislation and policy related specifically to technology. Subscribe to receive future editions.
MITRE’s Joe Ditre and Frank Ruscil demoed the code for the Comprehensive Careers and Supports for Households (C-CASH) at Rules as Code Demo Day. The MITRE team expanded the accessibility of the Policy Rules Database and the Cost-of-Living Database (the prior demo) by creating a web service API and a front-end Window’s application called C-CASH Analytic Tool (CAT). CAT provides a more scalable, flexible, and portable functionality which allows end-users to generate various households to run eligibility scenarios across different U.S. counties and states. They are currently working to create a national data hub and analytics tool, starting with utilizing U.S. Census data and populating the data warehouse by pushing large amounts of data through the PRD.
The report highlights a project to improve the Philadelphia Office of Homeless Services' (OHS) prevention and intake processes by implementing trauma-informed practices. In collaboration with staff, participants, and trauma experts, the project aimed to reduce distress for those accessing services while equipping staff with tools and training to better manage trauma-related interactions, creating a more supportive and empathetic service environment.
BenCon 2024 explored state and federal AI governance, highlighting the rapid increase in AI-related legislation and executive orders. Panelists emphasized the importance of experimentation, learning, and collaboration between government levels, teams, agencies, and external partners.
The team explored the performance of various AI chatbots and LLMs in supporting the adoption of Rules as Code for SNAP and Medicaid policies using policy data from Georgia and Oklahoma.
