This paper concludes that the substantial COVID-19 unemployment insurance expansion had limited disincentive effects on job searches, particularly among lower-income individuals, despite high wage replacement rates.
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 AI-powered explanation feature that effectively translates complex, multi-program policy calculations into clear and accessible explanations, enabling users to explore "what-if" scenarios and understand key factors influencing benefit amounts and eligibility thresholds.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
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.
An on-demand webinar showing how counties can design, fund, and measure local wage boost pilots to strengthen the child care workforce and support economic participation.
The team conducted experiments to determine whether clients would be responsive to proactive support offered by a chatbot, and identify the ideal timing of the intervention.
Tech Talent Project has partnered with American Enterprise Institute, the Beeck Center for Social Impact + Innovation at Georgetown University and New America to release a series of memos to help states make quick, meaningful progress in building technical capacity while avoiding past pitfalls.
