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.
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.
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.
We continued Rules as Code Demo Day with Daniel Singer and Preston Cabe from Benefits Data Trust. Benefits Data Trust provides benefit outreach and application assistance services in seven states. Using Benefits Launch, their in-house interview and rules engine, they support two hundred contact center employees as they screen and apply thousands of clients each year. They also offer a self-service screener, Benefits Launch Express. Additionally, they offer an eligibility API to integrate with other services.
mRelief is a nonprofit that helps individuals in all 53 U.S. states and territories determine SNAP eligibility and apply using easy-to-use web and text tools. Their simplified, inclusive approach has supported over 2.7 million people and unlocked over $1 billion in benefits, focusing on minimizing barriers and adapting eligibility rules across states.
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This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.