This explores how tax credit systems can be redesigned to better meet the needs of families, especially those facing systemic barriers to filing and receiving benefits.
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.
This report examines how the U.S. federal government can enhance the efficiency and equity of benefit delivery by simplifying eligibility rules and using a Rules as Code approach for digital systems.
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.
PolicyEngine US is a Python-based microsimulation model of the US tax and benefit system. It models federal individual income taxes (including credits), major benefit programs, and state income taxes (currently in six states). The PolicyEngine US package can be used as a Python package, via the PolicyEngine API, or via the policyengine.org web app.
The "Public Sector AI Playbook" provides public sector officers with practical guidance on adopting and implementing AI technologies to improve government operations, service delivery, and policymaking.
This review evaluates the UK public sector's use of digital technology, identifying successes and systemic challenges, and proposes reforms to enhance service delivery.
This provides a comprehensive look at child well-being across the U.S., ranking states and highlighting policy recommendations to improve outcomes for children.
There are frameworks available that could inform the standardization of communicating rules as code for U.S. public benefits programs. The Airtable communicates the differences between the frameworks and tools. Each entry is tagged with different categories that identify the type of framework or tool it is.
This is a searchable tool that compiles and categorizes over 4,700 policy recommendations submitted in response to the U.S. government's 2025 Request for Information on artificial intelligence policy.
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.
