This study examines how providing information about administrative burden influences public support for government programs like TANF, showing that awareness of these burdens can increase favorability toward the programs and their recipients.
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.
This study investigates how administrative burdens influence differential receipt of income transfers after a family member loses a job, looking at Unemployment Insurance, Temporary Assistance for Needy Families, and the Supplemental Nutrition Assistance Program.
This academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
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.
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
The article analyzes the impacts of Arkansas's Medicaid work requirements, finding that while coverage losses were reversed after the policy was halted, it did not improve employment and led to negative consequences such as increased medical debt and delayed care.
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.
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 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)
