Service Delivery Area: Benefits
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Facial Recognition Technology: Current Capabilities, Future Prospects, and Governance
This book explores the current capabilities, future possibilities, and necessary governance for facial recognition technology. The report discusses legal, societal, and ethical implications of the technology, and recommends ways that federal agencies and others developing and deploying the technology can mitigate potential harms and enact more comprehensive safeguards.
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Cross-Sector Insights From the Rules as Code Community of Practice
This report highlights key findings from the Rules as Code Community of Practice, including practitioners' challenges with complex policies, their desire to share knowledge and resources, the need for increased training and support, and a collective interest in developing open standards and a shared code library.
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Rules as Code Community of Practice
The DBN’s Rules as Code Community of Practice (RaC CoP) creates a shared learning and exchange space for people working on public benefits eligibility and enrollment systems — and specifically people tackling the issue of how policy becomes software code. The RaC CoP brings together cross-sector experts who share approaches, examples, and challenges. Participants are from state, local, tribal, territorial, and federal government agencies, nonprofit organizations, academia, and private sector companies. We host recurring roundtable conversations and an email group for asynchronous updates, insights, and assistance.
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What’s in a name? A survey of strong regulatory definitions of automated decision-making systems
The Electronic Privacy Information Center (EPIC) emphasizes the necessity of adopting broad regulatory definitions for automated decision-making systems (ADS) to ensure comprehensive oversight and protection against potential harms.
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What is a (Digital) Identity Wallet? A Systematic Literature Review
The report examines how current remote identity proofing methods can create barriers to Medicaid enrollment and suggests improvements to ensure equitable access for all applicants.
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What Are Generative AI, Large Language Models, and Foundation Models?
What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.
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The Privacy-Bias Trade-Off
Safeguarding privacy and addressing algorithmic bias can pose an under-recognized trade-off. This brief documents tradeoffs by examining the U.S. government’s recent efforts to introduce government-wide equity assessments of federal programs. The authors propose a range of policy solutions that would enable agencies to navigate the privacy-bias trade-off.
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The Ohio Benefits Program is “BOT” In
This award documentation from the National Association of State Chief Information Officers (NASCIO) explains how agencies in Ohio used automation to support administration of public benefits programs.
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The difference between digital identity, identification, and ID
This style guide from Caribou Digital outlines how to talk about identity in a digital age.
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Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing
This paper explores design considerations and ethical tensions related to auditing of commercial facial processing technology.
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Looking before we leap: Exploring AI and data science ethics review process
This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
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Large Language Models (LLMs): An Explainer
In this blog post, CSET’s Natural Language Processing (NLP) Engineer, James Dunham, helps explain LLMs in plain English.