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 consolidates learning and spotlights principles, insights, and emerging practices to guide municipal leaders and public-private partnerships interested in designing basic income programs that are ethical, equitable, rigorous, informative, and consequential for local, state and national policymaking.
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.
This page includes data and observations about authentication and identity proofing steps specifically for online applications that include child care applications.
This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
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.
The Playbook’s purpose is to guide researchers while supporting and lending authority to community organizations as they advocate for partnerships that will benefit their constituencies. The Playbook aims to provide some answers to such questions as: How can technologists and scientists engage communities in a spirit of partnership, without such extractive practices? How can community organizations work with researchers in ways that benefit their communities and expand their capacity, rather than burdening their staff?
This foundational article develops the concept of administrative burden, defining it as the learning, psychological, and compliance costs individuals face when interacting with government, and argues that these burdens are often shaped by political choices.
Journal of Public Administration Research and Theory
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.
This paper introduces a method for auditing benefits eligibility screening tools in four steps: 1) generate test households, 2) automatically populate screening questions with household information and retrieve determinations, 3) translate eligibility guidelines into computer code to generate ground truth determinations, and 4) identify conflicting determinations to detect errors.
