The guidelines for bias-free language contain both general guidelines for writing about people without bias across a range of topics and specific guidelines that address the individual characteristics of age, disability, gender, participation in research, racial and ethnic identity, sexual orientation, socioeconomic status, and intersectionality.
This handbook highlights the flexibilities in the Federal Acquisition Regulation (FAR) that can help agencies implement “plays” from the Digital Services Playbook, with a particular focus on how to use contractors to support an iterative, customer-driven software development process.
NIST has created a voluntary AI risk management framework, in partnership with public and private sectors, to promote trustworthy AI development and usage.
National Institute of Standards and Technology (NIST)
Benefits Data Trust (BDT), in collaboration with the Center for Health Care Strategies (CHCS), conducted a nationwide analysis of how states coordinate across Medicaid and SNAP programs to streamline access to benefits.
This Urban Institute article argues that poverty is driven by structural barriers rather than individual choices and advocates for safety net programs that address systemic inequities.
In 2024, the Center on Budget and Policy Priorities and Digital Benefits Network led a workshop to explore key terms related to digital identity, and provide ecosystem-level context on how authentication and identity proofing may show up in the online benefits experience and impact clients. This resource links to the presentation slides.
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
This report explains how the A-87 Exception enabled states to modernize and integrate health and human services systems, improving service delivery, efficiency, and data sharing across programs.
American Public Human Services Association (APHSA)
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)
