Code for America offers government agencies a general overview of getting started with implementing text messaging services for clear, responsive communication during the COVID-19 pandemic.
In this piece, the Digital Benefits Network shares several sources—from journalistic pieces, to reports and academic articles—we’ve found useful and interesting in our reading on automation and artificial intelligence.
SNAP Waivers and Adaptations During the COVID-19 Pandemic: A Survey of State Agency Perspectives in 2020 is a study conducted by the Johns Hopkins Institute for Health and Social Policy (IHSP) based at Johns Hopkins Bloomberg School of Public Health and the American Public Human Services Association (APHSA). This research seeks to understand perspectives from state SNAP administrators on the successes, challenges, and lessons learned from waivers and flexibilities used to preserve equitable access to SNAP during the COVID-19 pandemic. Based on state agency survey responses, this report summarizes key findings from the first calendar year of pandemic response and provides policy considerations for the future of SNAP. This research was supported by Healthy Eating Research, a national program of the Robert Wood Johnson Foundation.
Johns Hopkins Institute for Health and Social Policy
This toolkit provides individuals and organizations with guidance, drawn from learning and experience, on how to use administrative and other data to inform program improvements. It collects concrete strategies and practitioner-tested tools designed to advance these efforts. These materials were developed in pilot projects with local Temporary Assistance for Needy Families (TANF) agencies as part of the TANF Data Collaborative (TDC).
Our work with Pennsylvania to implement user experience and user interface changes shows that innovation can be easier to implement than it might seem.
This Urban Institute report explores the impact of benefit cliffs, plateaus, and trade-offs on families receiving public assistance, examining how changes in earnings affect access to essential benefits like SNAP, Medicaid, and housing subsidies.
County workers typically spend most of their time trying to get income information right during eligibility interviews. This article provides several recommendations for asking about income, accounting for cognitive biases, under-reporting, and complexities in reporting income.
This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
In May 2020, Stanford's HAI hosted a workshop to discuss the performance of facial recognition technologies that included leading computer scientists, legal scholars, and representatives from industry, government, and civil society. The white paper this workshop produced seeks to answer key questions in improving understandings of this rapidly changing space.
This report investigates how D.C. government agencies use automated decision-making (ADM) systems and highlights their risks to privacy, fairness, and accountability in public services.
