Intended Audience: Academic
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Red-Teaming in the Public Interest
This report explores how red-teaming practices can be adapted for generative AI in ways that serve the public interest.
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2025 Kids Count Data Book
This provides a comprehensive look at child well-being across the U.S., ranking states and highlighting policy recommendations to improve outcomes for children.
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Digital Identity Community of Practice 2025 Q3 Meeting: Beneficiary Feedback on Identity Proofing
On July 16, members of the Digital Identity Community of practice gathered to learn how peers are gathering beneficiary feedback on their experiences with accounts and proving their identity.
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Clients’ Perspectives on a Technology-Based Food Assistance Application System
This study examines how Florida’s transition to an online-only SNAP application system impacts accessibility and user experience.
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What Makes a Good AI Benchmark?
This brief presents a novel assessment framework for evaluating the quality of AI benchmarks and scores 24 benchmarks against the framework.
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Prioritizing Access and Safety Q&A on Service Design in Digital Identity
The Digital Benefit Network's Digital Identity Community of Practice held a session to hear considerations from civil rights technologists and human-centered design practitioners on ways to ensure program security while simultaneously promoting equity, enabling accessibility, and minimizing bias.
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Governing Digital Legal Systems: Insights on Artificial Intelligence and Rules as Code
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
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Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
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Technology, Data, and Design-Enabled Approaches for a More Responsive, Effective Social Safety Net
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
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Reducing Administrative Burdens: The U.S. Federal Government Framework
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.
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2024 Edition: Account Creation and Identity Proofing in Online Child Care Assistance Program (CCAP) Applications
This page includes data and observations about account creation and identity proofing steps specifically for online applications that include CCAP.
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AI-Powered Rules as Code: Experiments with Public Benefits Policy: Summary + Key Takeaways
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.