This report investigates how D.C. government agencies use automated decision-making (ADM) systems and highlights their risks to privacy, fairness, and accountability in public services.
Little is known about how agencies are currently using AI systems, and little attention has been devoted to how agencies acquire such tools or oversee their use.
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 toolkit provides resources for training and technical assistance (T/TA) providers in the Child Care Technical Assistance Network (CCTAN) to help State, Territory, and Tribal CCDF Lead Agencies be prepared to conduct equity assessments.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
For the past year, modernization teams at the Department of Labor (DOL) have been helping states identify opportunities to automate rote, non-discretionary, manual tasks, with the goal of helping them speed up the time that it takes to process claims. This post provides more context on Robotic Process Automation (RPA) and potential use cases in unemployment insurance.
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.
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 report summarizes progress made with agencies and members of the public to identify and reduce burdens that individuals, families, and small businesses face every day when interacting with government programs.
his document defines key terms, acronyms, and data elements used in Hawaii's SNAP and WIC data integration project to support cross-program collaboration and data standardization.
