Article describing the “time tax,” the costs to people applying or benefits in terms of spending substantial amounts of time navigating user-unfriendly interfaces. The article describes the necessity of simplifying safety-net programs and cross-coordinating across various social service programs.
This article describes the necessity of building an inclusive research environment that empowers participants, as well as techniques for creating such environments in both in-person and remote capacities.
This video shows you how to get started with using Generative AI tools, including Bard, Bing, and ChatGPT, in your work as public sector professionals.
Defining a product in government digital services is crucial, as it serves as the means through which a service is delivered to the public, and understanding its attributes ensures effective and continuous improvement.
This article examines how outdated state unemployment insurance (UI) systems struggled during the COVID-19 pandemic, leading to delays, technical failures, and widespread frustration for job seekers.
Government agencies adopting generative AI tools seems inevitable at this point. But there is more than one possible future for how agencies use generative AI to simplify complex government information.
Webinar that shares Nava’s partnership with the Gates Foundation and the Benefits Data Trust that seeks to answer if generative and predictive AI can be used ethically to help reduce administrative burdens for benefits navigators.
Ad Hoc has found that product operations can help scale impact by putting objective indicators at the center of product decision-making. The team has seen success in supporting product thinking at agencies like the Department of Veterans Affairs (VA), where they made it easier for Veterans to access employment and education assistance and for caregivers to receive needed support.
The Benefit Cliffs Calculator helps case managers and public benefit recipients to prepare for benefit cliffs (i.e., declines in benefits due to an increase in earnings). It compares the net resources and benefits available to families under different employment scenarios.
