As a part of Benefit Data Trust (BDT)’s Medicaid Churn Learning Collaborative, BDT has created a memo describing policy options and state examples for Medicaid administrators to reduce churn for non-MAGI Medicaid enrollees when the federal public health emergency ends.
The toolkit provides strategies for state and local WIC agencies to enhance enrollment by utilizing data from Medicaid and SNAP for cross-program data matching and targeted outreach.
A report that defines what effective “human oversight” of AI looks like in public benefits delivery and offers practical guidance for ensuring accountability, equity, and trust in algorithmic systems.
A blog post outlining key strategies states can use to lower SNAP payment error rates, a priority given new fiscal penalties tied to error rates under recent federal law.
An evaluation report assessing the performance of “Consult,” an AI-powered tool developed by the UK government’s Incubator for AI (i.AI), to analyse public consultation responses for the Scottish Government.
Government of Scotland Department for Science, Innovation and Technology (DSIT)
The AI Insights publication series from the UK Government’s Government Digital Service (GDS) provides technical guidance to help public sector organisations understand, implement, and manage artificial intelligence systems safely and effectively.
A public event that explored workers’ real-world experiences with unemployment insurance systems, focusing on barriers, inequities, and policy insights from their perspectives.
A public summary of findings from an AI trust study showing how Canadians perceive and trust different AI designs on the official government website, particularly preferring custom, transparent AI experiences over general third-party AI tools.
A practical implementation playbook guiding Healthy Start sites in designing, launching, and evaluating Alumni Peer Navigator (APN) services to improve maternal and child health through peer support and community-based participatory design.
An impact report summarizing how a small public-sector innovation team tested, built, and piloted shared digital services to reduce administrative burden in public benefits delivery.
This technical brief uses predictive analytics to identify the primary drivers of SNAP payment error rates (PER) following the implementation of the One Big Beautiful Bill (OBBB).
