This study examines how individuals assess administrative burdens and how these views change over time within the context of the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC).
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 research summary presents findings from a randomized controlled trial demonstrating how mRelief’s simplified SNAP application significantly increases application rates among eligible individuals.
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 report puts forth an anti-racist reimagining of Medicaid and CHIP that actively reckons with the racist history of the Medicaid program and offers principles and recommendations that capitalize on the transformative potential of the programs. The principles center the voices and agency of program participants and prioritize direct community involvement at all stages of the policy process.
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.
This academic paper examines how federal privacy laws restrict data collection needed for assessing racial disparities, creating a tradeoff between protecting individual privacy and enabling algorithmic fairness in government programs.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
Automated decision systems (ADS) are increasingly used in government decision-making but lack clear definitions, oversight, and accountability mechanisms.
