This resource describes how different agencies have updated their systems to increase online and mobile access to benefits information and applications, including using text messages to share benefits information with residents.
The Improving Service Delivery in EITC for New Yorkers initiative explores ways to enhance access to the Earned Income Tax Credit (EITC) through improved outreach, application processes, and service delivery.
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.
Research identified five key obstacles that researchers, activists, and advocates face in efforts to open critical public conversations about AI’s relationship with inequity and advance needed policies.
This article explores innovative strategies to improve access to public benefits by reducing administrative barriers and leveraging technology for a more user-friendly experience.
The Sprint 2 Report: Michigan UI Claimant Experience by Civilla and New America examines challenges in Michigan’s unemployment insurance (UI) system and provides human-centered design recommendations to improve accessibility, clarity, and user experience.
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.
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.
The experience of the COVID-19 pandemic and its induced recession underscored the crucial importance of unemployment insurance (UI) to workers, and to the stability of the American economy. Temporary federal expansions of unemployment systems during the pandemic showed how they can quickly be scaled to increase benefit levels and to include categories of workers who were not previously eligible, such as the self-employed, caregivers, and low-wage workers. And, states showed that separate programs can be set up to provide similar benefits to workers who are explicitly excluded from unemployment insurance—in particular immigrants who do not have a documented immigration status.
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 framework outlines USDA’s principles and approach to support States, localities, Tribes, and territories in responsibly using AI in the implementation and administration of USDA’s nutrition benefits and services. This framework is in response to Section 7.2(b)(ii) of Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
At Rules as Code Demo Day Executive Director Zareena Mayn and Chief Technology Officer Dize Hacioglu of mRelief demoed the code for their Supplemental Nutrition Assistance Program (SNAP) eligibility screener. mRelief is a women-led team that provides a web-based and text message-based SNAP eligibility screener to all 53 states and territories that participate in SNAP. They demonstrated how they have modularized their code to host federal program rules and state-specific rules.
