This blog explores the rise of person-centered insights in policymaking, featuring an overview of its benefits and expert interviews highlighting its crucial role in effectively delivering public benefits and human services.
This resource allows policymakers, employers, benefits providers, and researchers assess benefits performance for constituents and identify opportunities in market and policy innovation to ensure equitable benefits distribution.
Through deeply reported case studies and insights from focus groups, this report provides an in-depth look at the impact of pandemic-era government spending on families.
This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
This memo provides information to child and family service agencies on improving support for intersex children, adolescents, and their families through affirming practices, resources, and partnerships.
U.S. Department of Health and Human Services (HHS)
This framework provides voluntary guidance to help employers use AI hiring technology in ways that are inclusive of people with disabilities, while aligning with federal risk management standards.
This panel discussion from the Academy's 2025 Policy Summit explores the intersection of artificial intelligence (AI) and public benefits, examining how technological advancements are influencing policy decisions and the delivery of social services.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.
