This reporting explores how algorithms used to screen prospective tenants, including those waiting for public housing, can block renters from housing based on faulty information.
This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
This report outlines best practices for developing transparent, accessible, and standardized public sector AI use case inventories across federal, state, and local governments
These principles and best practices for AI developers and employers to center the well-being of workers in the development and deployment of AI in the workplace and to value workers as the essential resources they are.
This post argues that for the types of large-scale, organized fraud attacks that many state benefits systems saw during the pandemic, solutions grounded in cybersecurity methods may be far more effective than creating or adopting automated systems.
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.
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.
Hear perspectives on topics including centering beneficiaries and workers in new ways, digital service delivery, digital identity, and automation.This video was recorded at the Digital Benefits Conference (BenCon) on June 14, 2023.
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 is the summary version of a report that 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.
