A report that reviews what has been learned from guaranteed income pilot projects in Massachusetts and situates those findings within the broader national evidence base.
This FormFest profile explores how Philadelphia leveraged cross-department data sharing to launch its Zero Fare program, auto-enrolling eligible residents in unlimited transit benefits while tackling the challenge of outreach and trust-building to deliver passes effectively.
This case study documents how Civilla partnered with the Michigan Department of Health and Human Services (MDHHS) to redesign and modernize online enrollment for the state’s largest benefit programs.
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.
This resource introduces the "Layer Cake" approach, a framework for driving behavior change in government systems and public services by addressing all layers of service delivery, from frontline staff to policymakers, with an emphasis on human-centered design and civic participation.
Legislative Theatre brings residents, policymakers and activists together into creative, constructive dialogue, to co-create more equitable and effective policies and laws.
Public procurement in state governments can be slow and inefficient, but artificial intelligence (AI) offers a solution by automating tasks, improving decision-making, and addressing workforce gaps, as highlighted in a joint brief by NASCIO and NASPO.
National Association of State Chief Information Officers (NASCIO)
The team explored the performance of various AI chatbots and LLMs in supporting the adoption of Rules as Code for SNAP and Medicaid policies using policy data from Georgia and Oklahoma.
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to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
This study explores the causal impacts of income on a rich array of employment outcomes, leveraging an experiment in which 1,000 low-income individuals were randomized into receiving $1,000 per month unconditionally for three years, with a control group of 2,000 participants receiving $50/month.
