A unified taxonomy and tooling suite that consolidates AI risks across frameworks and links them to datasets, benchmarks, and mitigation strategies to support practical AI governance.
A case study describing how Massachusetts is building long-term public-sector capacity to deliver people-centered digital services by strengthening in-house expertise, shared tools, and agency-embedded support.
A national survey of low-wage workers showing that administrative burdens in SNAP and Medicaid are common and strongly linked to food hardship, healthcare hardship, and chronic illness.
A recent study challenges the common belief that income support programs like SNAP reduce employment, finding that for individuals with a work history, receiving SNAP benefits can actually increase long-term employment.
The article examines the impact of digital interfaces on welfare state administration, focusing on the UK's Universal Credit system and the design elements that shape user interactions and behavior in an "interface first" bureaucracy.
This article explores how integrating behavioral science into public administration can improve government effectiveness, equity, and trust by redesigning public services with human behavior in mind.
This paper describes results from fieldwork conducted at a social services site where the workers evaluate citizens' applications for food and medical assistance submitted via an e-government system. These results suggest value tensions that result - not from different stakeholders with different values - but from differences among how stakeholders enact the same shared value in practice.
CHI '14: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
This paper examines three key questions in participatory HCI: who initiates, directs, and benefits from user participation; in what forms it occurs; and how control is shared with users, while addressing conceptual, ethical, and pragmatic challenges, and suggesting future research directions.
The Better Government Lab at the McCourt School of Public Policy at Georgetown University has developed a new scale for measuring the experience of burden when accessing public benefits. They offer both a three-item scale and a single-item scale, which can be utilized for any public benefit program. The shorter scales provide a less burdensome way to measure by requiring less information from users.
Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. This academic study presents an approach to evaluate bias present in automated facial analysis algorithms and datasets.
Well-designed, user-focused tools that allow for simple application are key to ensuring that families most in need receive the Child Tax Credit. Reaching these households will require a robust effort from the IRS to create user-friendly tools in partnership with organizations with a direct connection to eligible recipients.
