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.
The government briefing included:
- Ways AI may be answering beneficiary questions
- Methods for evaluating AI models
- Wins and challenges with AI + automation
- Ensuring that policy manuals are ready for AI
- Exploring AI for writing code for eligibility & enrollment systems
Speakers from the Georgetown University research team:
- Ariel Kennan, Senior Director, Digital Benefits Network, Beeck Center for Social Impact + Innovation
- Lisa Singh, Director, Massive Data Institute (MDI), Sonneborn Chair and Professor, Department of Computer Science and McCourt School of Public Policy
- Jason Goodman, MPP/MBA Candidate
- Mohamed Ahmed, PhD Candidate
- Alessandra Garcia Guevara, Computer Science Undergraduate Candidate
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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.
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.
