Topic: Research
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Exploring a new way to make eligibility rules easier to implement
Programs like Medicaid and SNAP are managed at the federal level, administered at the state level, and often executed at the local level. Because there are so many in-betweens, there is significant duplicated effort, demonstrating the need to simplify eligibility rules to facilitate easier implementation.
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Shared Values/Conflicting Logics: Working Around E-Government Systems
This paper describes results from fieldwork conducted at a social services site where the workers evaluate citizens' applications for food and medical assistance submitted via an e-government system. These results suggest value tensions that result - not from different stakeholders with different values - but from differences among how stakeholders enact the same shared value in practice.
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AI-Driven Statutory Reasoning via Software Engineering Methods
This article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
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Helping People with Low Incomes Navigate Benefit Cliffs: Lessons Learned Deploying a Marginal Tax Rate Calculator
This report details findings and lessons from a project to develop a calculator to help people anticipate how a change in earnings from employment would affect their net income and information on their estimated effective marginal tax rate.
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Envisioning a Human-AI collaborative system to transform policies into decision models
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
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Envisioning a Federal Rules as Code Approach to Public Benefits Eligibility
Digitizing public benefits policy will make the biggest impact for administrators and Americans, but only if it happens at the highest level of government.
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Recap: Digital Identity Community of Practice Kick-Off
The Digital Identity Community of Practice kick-off event featured key resources, a new research publication on account creation and identity proofing, and insights from multiple speakers.
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Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net
This report examines how the U.S. federal government can enhance the efficiency and equity of benefit delivery by simplifying eligibility rules and using a Rules as Code approach for digital systems.
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AI-Powered Rules as Code Government Briefing
The DBN and MDI held a government briefing of their top takeaways from their recent research on AI-Powered Rules as Code.
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Exposing Error in Poverty Management Technology: A Method for Auditing Government Benefits Screening Tools
This paper introduces a method for auditing benefits eligibility screening tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors.
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Cracking the code: Rulemaking for humans and machines
The OECD report explores the concept of "Rules as Code" (RaC), proposing a transformation in government rulemaking by developing machine-consumable regulations alongside human-readable versions.
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Digital Distortions and Interpretive Choices: A Cartographic Perspective on Encoding Regulation
This article analyses ‘digital distortions’ in Rules as Code, which refer to disconnects between regulation and code that arise from interpretive choices in the encoding process.