Usability tests can help teams develop products that are user-centered, accessible, and inclusive. This guide will help you create a robust plan to promote a successful usability test.
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.
This article from Civil Eats explores how expanding online purchasing options for SNAP recipients can improve food security, especially in the wake of the COVID-19 pandemic.
This article explores ongoing efforts to modernize state unemployment insurance (UI) systems, addressing long-standing inefficiencies and challenges exposed by the COVID-19 pandemic.
This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
Public procurement in state governments can be slow and inefficient, but artificial intelligence (AI) offers a solution by automating tasks, improving decision-making, and addressing workforce gaps, as highlighted in a joint brief by NASCIO and NASPO.
National Association of State Chief Information Officers (NASCIO)
Well-designed, user-focused tools that allow for simple application are key to ensuring that families most in need receive the Child Tax Credit. Reaching these households will require a robust effort from the IRS to create user-friendly tools in partnership with organizations with a direct connection to eligible recipients.
This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
The Modern Government Leaders program brought together federal executive innovators to share best practices and drive service delivery and process modernization.
This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
