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.
The Digital Identity Community of Practice kick-off event featured key resources, a new research publication on account creation and identity proofing, and insights from multiple speakers.
beta.gouv.fr, a French government incubator, developed Mes Aides, an online benefits simulator launched in 2014 to help residents assess their eligibility for various social programs, addressing the issue of unclaimed benefits. The tool, built with open-source technology, enabled users to quickly estimate their potential benefits but was later integrated into a broader platform in 2020 following internal government disputes over authority.
Alluma is a nonprofit that provides digital solutions to simplify eligibility screening and enrollment for social benefit programs, supporting cross-benefit access in 45 counties and two states. Their One-x-Connection product suite streamlines Medicaid and SNAP applications using a business rules engine, with a focus on human-centered design and anonymous, simplified eligibility checks, having helped screen over 10 million individuals and submitted over 67 million applications.
In this updated primer, the DBN describes how identity proofing and authentication show up in public benefits applications and outlines equity and security concerns raised by common identity proofing and authentication methods.
The team developed an application to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
A comprehensive resource guide providing an overview of mobile driver’s licenses (mDLs) in the United States, including their implementation status, technical standards, and key privacy and accessibility considerations.
This report highlights key findings from the Rules as Code Community of Practice, including practitioners' challenges with complex policies, their desire to share knowledge and resources, the need for increased training and support, and a collective interest in developing open standards and a shared code library.
The examples in this guide describe how peer-to-peer training and updated interview scripts can help connect residents to the benefits they are eligible for.
