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.
This publication explains the fundamentals of state IEE systems—including the technology, opportunities, risks, and stakeholders involved. It is a resource for state officials, advocates, funders, and tech partners working to implement these systems.
A nationwide survey capturing how state chief data officers are structured, resourced, and operating, and how the role is evolving to support data-driven government.
National Association of State Chief Information Officers (NASCIO)
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.
An event recap from one of FormFest 2024's opening main stage featuring speakers from the Commonwealth of Pennsylvania, State of Maryland, Beeck Center for Social Impact + Innovation, and Code for America.
This article highlights how the City of Saint Paul and Ramsey County are advancing equitable access to climate-resilient green careers through their participation in TOPCities.
Presentation covering the findings of a research study analyzing the structural and budgetary layout of of eleven US-based Digital Service Teams (DSTs) at the municipal, county, and state levels.
Michigan's UIA director, Julia Dale, is leading the agency through transition by prioritizing lived experience, hope, grit, and values. Virginia's SNAP Program Manager, Michele Thomas, highlighted the success of Sun Bucks, a summer EBT child nutrition program that fed over 700,000 kids in its first year.
The Better Government Lab at the McCourt School of Public Policy at Georgetown University has developed a new scale for measuring the experience of burden when accessing public benefits. They offer both a three-item scale and a single-item scale, which can be utilized for any public benefit program. The shorter scales provide a less burdensome way to measure by requiring less information from users.
This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
