Code for America’s Integrated Benefits Initiative has been working in partnership with the State of Colorado to demonstrate how user-centered approaches lead to measurably better delivery of safety net programs. This article describes their work with the state of Colorado in simplifying how clients report common life changes that can affect their eligibility.
It is frequently assumed that when rules are implemented as code, a rules engine is necessary. However, it is possible for policy people and engineers to effectively work together to code logic that drives technological system without needing a mediating rules engine at all.
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.
18F describes modular contracting, the process of breaking up large, custom software procurements into a small constellation of smaller contracts. Modular procurement requires agile, product thinking, user-centered design, DevSecOps, and loosely-coupled architecture.
Study by the Director of the Office of Management and Budget assessing methods for determining whether agency policies and actions create or exacerbate barriers to full and equal participation by eligible individuals. This study followed the Executive Order on racial equity.
This brief highlights key takeaways from APHSA’s work on young families, starting with an overview of the young families work and its early years, followed by key takeaways and highlights from its final year, ending with opportunities for future work in the young families space.
American Public Human Services Association (APHSA)
Professor Don Moynihan discusses how administrative burden is an effective tool to make it difficult for people to access certain types of benefits, noting that this is particularly harmful to communities of color.
APHSA explains how certain tools and recommendations about when people apply for help, engage in services, and maintain benefits can have a powerful effect to either counter or exacerbate structural barriers to accessing assistance.
American Public Human Services Association (APHSA)
This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
In this interview, Code for America staff members share how client success, data science, and qualitative research teams work together to consider the responsible deployment of artificial intelligence (AI) in responding to clients who seek assistance with three products.
What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.
Center for Security and Emerging Technology (CSET)
