This piece highlights promising design patterns for account creation and identity proofing in public benefits applications. The publication also identifies areas where additional evidence, resources, and coordinated federal guidance may help support equitable implementations of authentication and identity proofing, enabling agencies to balance access and security.
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.
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 study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
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.
In February 2023, the Digital Benefits Network at the Beeck Center for Social Impact + Innovation released a dataset documenting authentication and identity verification requirements that unemployment insurance (UI) applicants encounter across the United States. This resource outlines high-level observations from the data and more information about the research process.
This guide provides practical insights for benefits administrators on redesigning benefits systems using human-centered design to ensure all eligible residents can access crucial social safety net resources.
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.
This guide discusses general characteristics shared by organizations that have successfully created accessible content, and includes case studies that showcase characteristics of successful accessible content teams.
