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 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.
This report highlights key findings from the Rules as Code Community of Practice, including practitioners' challenges with complex policies, their desire to share knowledge and resources, the need for increased training and support, and a collective interest in developing open standards and a shared code library.
This brief describes TDI’s efforts to transform federal TANF and employment data into an integrated resource for program management and evidence building.
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.
This report examines how the U.S. federal government can enhance the efficiency and equity of benefit delivery by simplifying eligibility rules and using a Rules as Code approach for digital systems.
This page includes data and observations about authentication and identity proofing steps specifically for online applications that include MAGI Medicaid.
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.
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.
Based on state agency survey responses, this report summarizes key findings from the first calendar year of pandemic response and provides policy considerations for the future of SNAP.
American Public Human Services Association (APHSA)
This book is an in-depth exploration of federal programs and controversial legislation demonstrating that administrative burden has long existed in policy design, preventing citizens from accessing fundamental rights. Further discussion of how policymakers can minimize administrative burden to reduce inequality, boost civic engagement, and build an efficient state.
