Topic: Research
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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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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-Powered Rules as Code: Experiments with Public Benefits Policy
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.
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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.
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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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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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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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Applying Rules as Code to the Social Safety Net
This short report outlines the promise and potential of digitizing benefits eligibility policy.