In February 2023, the Digital Benefits Network at the Beeck Center for Social Impact + Innovation released a dataset documenting authentication and identity verification requirements that unemployment insurance (UI) applicants encounter across the United States. This resource outlines high-level observations from the data and more information about the research process.
This session from FormFest 2024 focused on human centered form improvements from the City of Reykjavik and the German Digital Service’s form simplification project.
The State Chief Data Officer Tracker, created by the Beeck Center’s Digital Service Network and Data Labs teams, is a first-of-its-kind resource that tracks the evolving role of CDOs in state governments and their efforts to advance data-informed decision-making and collaboration across agencies.
The Long Beach Tree Map shows trees throughout the Long Beach region which centralizes, organizes, and visualizes information regarding where and how many trees as well as their type.
DSN Spotlights are short-form project profiles that feature exciting work happening across our network of digital government practitioners. Spotlights celebrate our members’ stories, lift up actionable takeaways for other practitioners, and put the resources + examples we host in the Digital Government Hub in context.Â
The Digital Service Network (DSN) spoke with GC Forms’ Senior Product Manager Stevie-Ray Talbot and Acting Head Ioana Contu to learn more about the team's approach to building GC Forms.
This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
This article analyzes the strategic use of public policy as a tool for reshaping public opinion. Though progressive revisionists in the 1990s argued that reforming welfare could produce a public more willing to invest in anti-poverty efforts, welfare reform in the 1990s did little to shift public opinion. This study investigates the general conditions under which mass feedback effects should be viewed as more or less likely.
