This report poses the question of whether states are prepared to meet the new Medicaid work reporting and renewal mandates introduced by HR 1, given ongoing strain from the post-pandemic “unwinding.”
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.
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)
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
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.
This landscape analysis examines data, design, technology, and innovation-enabled approaches that make it easier for eligible people to enroll in, and receive, federally-funded social safety net benefits, with a focus on the earliest adaptations during the COVID-19 pandemic.
This report analyzes the critical role of SNAP’s "broad-based categorical eligibility" (BBCE) policy and the widespread consequences of its potential elimination by the Trump Administration.
A blog recap of a FormFest session on improving digital form accessibility, with a focus on accessible error handling and WCAG 2.1 compliance under Title II requirements.
On July 16, members of the Digital Identity Community of practice gathered to learn how peers are gathering beneficiary feedback on their experiences with accounts and proving their identity.
