This case study examines how Michigan’s Department of Health and Human Services uses data practices to advance racial equity in child welfare through identity-informed data collection and anonymous decision-making.
U.S. Department of Health and Human Services (HHS)
This guide provides practical financing strategies for governments to build, maintain, and expand integrated data systems (IDS) and evaluation capacity using federal and non-federal funding sources.
This one-pager introduces Iowa’s Child Care Data Dashboards, which provide near real-time insights into child care supply, demand, and vacancies to support data-informed planning across the state.
This one-pager introduces Iowa Child Care Connect (C3), a centralized data system that integrates near-real-time child care data to support families, providers, policymakers, and economic development efforts across the state.
An updated guide for public sector and civic data users to embed racial equity and community voice throughout the data life cycle—from planning to dissemination.
A policy directive that establishes standards and guidance for federal executive agencies to manage, secure, and deliver public websites and digital services that are user-centered, accessible, and data-driven.
A strategy document that sets out USAID’s vision, goals, principles, and operational requirements for promoting open, inclusive, secure, and rights-respecting digital ecosystems to advance development and humanitarian outcomes worldwide.
United States Agency International Development (USAID)
This report provides an overview of the task force’s work in assessing, guiding, and recommending policies for the safe, ethical, and effective use of generative AI across Alabama’s executive-branch agencies.
State of Alabama Generative Artificial Intelligence (GenAI) Task Force
This profile provides a cross-sectoral profile of the AI Risk Management Framework specifically for Generative AI (GAI), outlining risks unique to or exacerbated by GAI and offering detailed guidance for organizations to govern, map, measure, and manage those risks responsibly.
National Institute of Standards and Technology (NIST)
Guidance on improving how well AI systems can understand digital content. It emphasizes using machine-readable formats and applying clear content design strategies to enhance both AI processing and human accessibility
Digital IDs can improve convenience, but they risk surveillance, data misuse, and exclusion if not designed with privacy, security, and accessibility safeguards.
