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.
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.
This interview template includes questions designed to help teams conduct exploratory, semi-structured interviews with government stakeholders involved in program delivery to gather information that can help them evaluate the status quo of digital delivery in their organization.
Michigan's UIA director, Julia Dale, is leading the agency through transition by prioritizing lived experience, hope, grit, and values. Virginia's SNAP Program Manager, Michele Thomas, highlighted the success of Sun Bucks, a summer EBT child nutrition program that fed over 700,000 kids in its first year.
Sarah Bargal provides an overview of AI, machine learning, and deep learning, illustrating their potential for both positive and negative applications, including authentication, adversarial attacks, deepfakes, generative models, personalization, and ethical concerns.
NYC's My File NYC and New Jersey's unemployment insurance system improvements demonstrate how successful digital innovations can be scaled across various programs, leveraging trust-building, open-source technology, and strategic partnerships.
A recap of the two-day conference focused on charting the course to excellence in digital benefits delivery hosted at Georgetown University and online.
This session from FormFest 2024 featured the work in Austin, Texas on criminal justice forms, and the South Bend, Indiana Animal Resource Center’s efforts to redevelop their animal adoption forms.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
This paper outlines the need for comprehensive reforms to improve the U.S. government's capacity to effectively implement policies, focusing on reducing bureaucratic inefficiencies, enhancing workforce structures, and leveraging digital infrastructure.
