This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
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 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.
An America where no one experiences poverty is possible. Already, the U.S. has programs with the potential to make this vision a reality, including programs that provide cash assistance, like Temporary Assistance for Needy Families (TANF). The current TANF program provides very little cash assistance and is marked by stark racial disparities, but it has the potential to reduce child poverty, increase economic security, and advance racial equity. This report offers a vision for an anti-racist approach to the TANF program, with new statutory goals and policy recommendations to advance racial justice.
This guidebook aims to equip state and local agencies with the practical insights they need to develop a text messaging outreach program for SNAP recertification.
This report explores technologies that have the potential to significantly affect employment and job quality in the public sector, the factors that drive choices about which technologies are adopted and how they are implemented, how technology will change the experience of public sector work, and what kinds of interventions can protect against potential downsides of technology use in the public sector. The report categories technologies into five overlapping categories including manual task automation, process automation, automated decision-making systems, integrated data systems, and electronic monitoring.
Building on our February 2022 report Benefit Eligibility Rules as Code: Reducing the Gap Between Policy and Service Delivery for the Safety Net, the Beeck Center’s Digital Benefits Network (DBN) hosted Rules as Code Demo Day on June 28, 2022 where there were eight demonstrations of projects and code followed by a collaborative problem solving session on how to continue advancing rules as code for the U.S. social safety net.
In December 2024, the Digital Benefits Network released an updated open dataset on authentication and identity proofing requirements across various public benefits applications to highlight best practices and areas for improvement in identity management.
This session from FormFest 2024 focused on how to help people get the assistance they need from the U.S. Department of Health and Human Services’ work on the Low Income Home Energy Assistance Program (LIHEAP) and the Maryland Social Services Administration’s work to improve welfare support for kinship caregivers.
This brief outlines the U.S. federal government’s framework to identify, reduce, and address administrative burdens through a series of executive orders, legislative actions, and updated policies focused on improving customer experience and increasing access to government benefits.
The State Chief Data Officer Tracker, created by the Beeck Center’s Digital Service Network and Data Labs teams, is a first-of-its-kind resource that tracks the evolving role of CDOs in state governments and their efforts to advance data-informed decision-making and collaboration across agencies.
