Errors in administrative processes are costly and burdensome for clients but are understudied. Using U.S. Unemployment Insurance data, this study finds that while automation improves accuracy in simpler programs, it can increase errors in more complex ones.
AI resources for public professionals on responsible AI use, including a course showcasing real-world applications of generative AI in public sector organizations.
This course is designed to help public professionals accelerate the process of finding and implementing urgently-needed evidence-based solutions to public problems.
A comprehensive series of workshops and courses designed to equip public sector professionals with the knowledge and skills to responsibly integrate AI technologies into government operations.​
BenCon 2024 explored state and federal AI governance, highlighting the rapid increase in AI-related legislation and executive orders. Panelists emphasized the importance of experimentation, learning, and collaboration between government levels, teams, agencies, and external partners.
The study investigates how state agencies administering SNAP comply with Title VI of the Civil Rights Act by providing language access for individuals with limited English proficiency (LEP).
The Policy2Code Prototyping Challenge explored utilizing generative AI technology to translate U.S. government policies for public benefits into plain language and code, culminating in a Demo Day where twelve teams showcased their projects for feedback and evaluation.
The team introduced "Policy Pulse," a tool to help policy analysts understand laws and regulations better by comparing current policies with their original goals to identify implementation issues.
The team examined how AI, specifically LLMs, could streamline the case review process for SNAP applications to alleviate the burden on case workers while potentially improving accuracy.
The team explored using LLMs to interpret the Program Operations Manual System (POMS) into plain language logic models and flowcharts as educational resources for SSI and SSDI eligibility, benchmarking LLMs in RAG methods for reliability in answering queries and providing useful instructions to users.
The team developed an application to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
