This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
The report examines how current remote identity proofing methods can create barriers to Medicaid enrollment and suggests improvements to ensure equitable access for all applicants.
Annual Computers, Software, and Applications Conference (COMPSAC)
This article explores how anticipatory logics—drawing from foresight, futures thinking, and design—are shaping the future of government by creating space for innovative policy approaches, public participation, and proactive governance.
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 article examines the effects of Arkansas’s Medicaid work requirements, finding substantial coverage losses and no significant increase in employment, compounded by widespread confusion among beneficiaries about the policy.
This paper examines the challenges U.S. state and local digital service teams face in retaining talent and offers strategies to improve retention and team stability.
In the article, researchers examines how administrative burdens in waitlist management for subsidized childcare in Massachusetts have led to significant reductions in the number of families awaiting assistance, potentially obscuring the true extent of unmet need.
This brief analyzes the current state of federal and state government communication around benefits eligibility rules and policy and how these documents are being tracked and adapted into code by external organizations. This work includes comparisons between coded examples of policy and potential options for standardizing code based on established and emerging data standards, tools, and frameworks.
This research summary presents findings from a randomized controlled trial demonstrating how mRelief’s simplified SNAP application significantly increases application rates among eligible individuals.
This research explores how software engineers are able to work with generative machine learning models. The results explore the benefits of generative code models and the challenges software engineers face when working with their outputs. The authors also argue for the need for intelligent user interfaces that help software engineers effectively work with generative code models.
