This mainstage session from FormFest 2024 included conversations about form design, accessibility, user experience, and data collection to show how good forms can build trust and confidence in government.
This framework is a logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data.
This annotated bibliography compiles key resources on data linkage and integration for research and statistical purposes, focusing on best practices, governance, and technical considerations.
This report provides a comprehensive analysis of administrative burdens, offering strategies to reduce unnecessary obstacles in public service delivery, with a focus on improving access to government services for underserved and marginalized populations.
The State Chief Data Officer Tracker, created by the Beeck Center’s Digital Service Network and Data Labs teams, is a first-of-its-kind resource that tracks the evolving role of CDOs in state governments and their efforts to advance data-informed decision-making and collaboration across agencies.
A collaborative resource document detailing the civic tech support offerings, state and local government resources, and civic tech organizational support.
Monthly SNAP participation data for the United States and every state, from October 1988 through the latest month published by USDA Food and Nutrition Service (generally a 3-month lag).
This report offers a detailed assessment of how AI and emerging technologies could impact the Social Security Administration’s disability benefits determinations, recommending guardrails and principles to protect applicant rights, mitigate bias, and promote fairness.
This guide introduces privacy-enhancing technologies (PETs) and provides practical guidance for government agencies on selecting and implementing them to securely use, share, and protect sensitive data.
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.
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)
