This report analyzes the growing use of generative AI, particularly large language models, in enabling and scaling fraudulent activities, exploring the evolving tactics, risks, and potential countermeasures.
Led by the Digital Benefits Network in partnership with Public Policy Lab, the Digital Doorways research project amplifies the lived experiences of beneficiaries to provides new insights into people’s experiences with digital identity processes and technology in public benefits. This executive summary gives an overview of the project’s findings.
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
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.
This brief outlines the U.S. federal government’s framework to identify, reduce, and address administrative burdens through a series of executive orders, legislative actions, and updated policies focused on improving customer experience and increasing access to government benefits.
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 report summarizes insights from interviews with seven states on how they are adapting integrated eligibility and enrollment (IEE) systems in response to sweeping federal changes to SNAP and Medicaid under H.R. 1.
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.
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.
