This paper describes the policy choices, business practices, and technology innovations that the State of New Jersey is employing to ensure that the right people get benefits — accurately and on time.
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
The examples in this guide describe how peer-to-peer training and updated interview scripts can help connect residents to the benefits they are eligible for.
This report analyzes the growing use of generative AI, particularly large language models, in enabling and scaling fraudulent activities, exploring the evolving tactics, risks, and potential countermeasures.
This report documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
Artificial intelligence promises exciting new opportunities for the government to make policy, deliver services and engage with residents. But government procurement practices need to adapt if we are to ensure that rapidly-evolving AI tools meet intended purposes, avoid bias, and minimize risks to people, organizations, and communities. This report lays out five distinct challenges related to procuring AI in government.
The New Jersey Department of Human Services and New Jersey Department of Health collaborated in their Coordinating SNAP & Nutrition Supports project to enhance the enrollment and coordination of SNAP and WIC programs.
American Public Human Services Association (APHSA)
This report by EPIC investigates how automated decision-making (ADM) systems are used across Washington, D.C.’s public services and the resulting impacts on equity, privacy, and access to benefits.
