Created for use in the Digital Doorways research project, this design stimuli shows the steps of submitting an application, sharing personal information, and verifying identity for Arizona's online application for Unemployment Insurance.
A unified taxonomy and tooling suite that consolidates AI risks across frameworks and links them to datasets, benchmarks, and mitigation strategies to support practical AI governance.
Created for use in the Digital Doorways research project, this design stimuli shows the steps of submitting an application, sharing personal information, and verifying identity for Massachusetts' online application for Unemployment Insurance.
An interactive dashboard that enables users to explore and monitor key metrics of the Supplemental Nutrition Assistance Program (SNAP) Quality Control (QC) system.
Recapping the work and achievements of the Digital Benefits Network (DBN), Digital Service Network (DSN), and the State Chief Data Officers Network (CDO) in 2025.
Practitioner Picks is a quarterly series designed to add fresh resources to the Digital Government Hub’s library, helping people improve government digital service delivery. Each issue spotlights resources chosen by practitioners in a specific service delivery area along with their insights on why these picks are valuable additions to the Hub.
A webinar presenting fresh data on how young adults aged 22 are faring in terms of poverty, employment, education, living arrangements, and access to public benefits.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
This session from FormFest 2024 walked attendees through some of the major changes AI is bringing to form design. Learn about the National Head Start Association’s use of AI to reduce administrative burden and the Canadian Digital Service’s tips for protecting government applications systems from AI.
The Digital Service Network worked closely with stakeholders from the Texas Education Academy (TEA) to develop resources for a structured approach in helping identify and better understand core challenges in government digital delivery.
This academic paper examines how federal privacy laws restrict data collection needed for assessing racial disparities, creating a tradeoff between protecting individual privacy and enabling algorithmic fairness in government programs.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
