The experience of the COVID-19 pandemic and its induced recession underscored the crucial importance of unemployment insurance (UI) to workers, and to the stability of the American economy. Temporary federal expansions of unemployment systems during the pandemic showed how they can quickly be scaled to increase benefit levels and to include categories of workers who were not previously eligible, such as the self-employed, caregivers, and low-wage workers. And, states showed that separate programs can be set up to provide similar benefits to workers who are explicitly excluded from unemployment insurance—in particular immigrants who do not have a documented immigration status.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This framework outlines USDA’s principles and approach to support States, localities, Tribes, and territories in responsibly using AI in the implementation and administration of USDA’s nutrition benefits and services. This framework is in response to Section 7.2(b)(ii) of Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
At Rules as Code Demo Day Executive Director Zareena Mayn and Chief Technology Officer Dize Hacioglu of mRelief demoed the code for their Supplemental Nutrition Assistance Program (SNAP) eligibility screener. mRelief is a women-led team that provides a web-based and text message-based SNAP eligibility screener to all 53 states and territories that participate in SNAP. They demonstrated how they have modularized their code to host federal program rules and state-specific rules.
Inclusive design means making design choices that take into account personal identifiers like ability, race, economic status, language, age, and gender. This resource walks folks through an introduction to inclusive design, focusing on accessibility and equity.
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.
This guidebook aims to equip state and local agencies with the practical insights they need to develop a text messaging outreach program for SNAP recertification.
The pandemic has shown how difficult it can be for the US to succeed with major technology projects. Various leading design thinkers discuss strategies for building more efficient and effective government technology.
Together, the Kansas Department for Children and Families (DCF) and Department of Health and Environment (KDHE) are working to design and build a sustainable process to improve cross-enrollment for families eligible for both the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). This report outlines how Kansas will integrate data matches between SNAP and WIC—as well as targeted outreach— within the ongoing business processes of the agencies to help streamline the experience of accessing nutrition supports for clients. These functions will contribute to the agencies’ shared goal of reducing rates of food insecurity in Kansas.
American Public Human Services Association (APHSA)
New America’s New Practice Lab discusses insights into racial inequities within the Unemployment Insurance System, and provides recommendations its framework for rectifying inequality in policies and programs moving forward.
