Building on our February 2022 report Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net, the Beeck Center’s Digital Benefits Network (DBN) hosted Rules as Code Demo Day on June 28, 2022 where there were eight demonstrations of projects and code followed by a collaborative problem solving session on how to continue advancing rules as code for the U.S. social safety net.
At Rules as Code Demo Day we heard from Song Hia of the NYC Mayor’s Office for Economic Opportunity and Ethan Lo of the NYC Office of Technology and Innovation who demoed the NYC Benefits Platform Screening API which provides machine-readable calculations and criteria for benefits screening that power the ACCESS NYC screening questionnaire. This makes it easier for NYC residents to discover multiple benefits they may be eligible for. The City is now extending the API to support the new MyCity platform, a one-stop shop for all services and benefits.
During the call, we heard from two speakers: April Dunlap, Policy Administrator for Arizona’s Department of Economic Security and Professor Michele Gilman, Venable Professor of Law and Associate Dean for Faculty Research and Development at the University of Baltimore School of Law.
The first half of Rules as Code Demo Day was wrapped up with Thomas Guillet who has contributed to Open Fisca France and beta.gouv. He demoed the code for Mes Aides—or My Benefits—which is France’s social benefit simulator that leverages open source rule models for over 600 benefits while keeping the displayed complexity to its minimum.
This resource provides examples and practical guides that explain how to use existing regulations and data sharing agreements to transfer client information or eligibility status between benefit programs.
In response to COVID-19, the Workers Lab and Steady developed the "Income Passport" to streamline gig workers' unemployment benefit applications by pulling income data directly from gig platforms and financial accounts. This tool reduced manual verification time, helped prevent fraud, and improved workers' access to full benefits, with successful tests in Alabama and Louisiana demonstrating significant time savings and improved service delivery.
Sarah Bargal provides an overview of AI, machine learning, and deep learning, illustrating their potential for both positive and negative applications, including authentication, adversarial attacks, deepfakes, generative models, personalization, and ethical concerns.
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
This video documents the Digital Benefits Network's Digital Identity Community of Practice launch, covering mission review, 2025 goals, California authentication innovations, and peer networking for equitable and effective digital identity in public benefits.
This collection of research references is designed to support government agencies designing public-facing identity management processes that meet people’s needs.
