This academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.
This guide is intended to provide a general overview of the national statutory and regulatory landscape governing the legality of sending large volumes of text messages and sharing client information.
Concerns over risks from generative artificial intelligence systems have increased significantly over the past year, driven in large part by the advent of increasingly capable large language models. But, how do AI developers attempt to control the outputs of these models? This primer outlines four commonly used techniques and explains why this objective is so challenging.
Center for Security and Emerging Technology (CSET)
Well-designed, user-focused tools that allow for simple application are key to ensuring that families most in need receive the Child Tax Credit. Reaching these households will require a robust effort from the IRS to create user-friendly tools in partnership with organizations with a direct connection to eligible recipients.
This page includes data and observations about authentication and identity proofing steps specifically for online applications that include child care applications.
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.
Teams crafting policy inside and outside government can use the assessment to center their policy-making activities around those most impacted by their proposed programs and policy ideas.
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.
