This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
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.
The Maryland Information Technology Master Plan 2025 lays out the state’s strategy to modernize IT, expand digital services, and strengthen infrastructure to better serve residents and government agencies.
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.
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 publication seeks to answer one of the most common questions that CIOs ask: “What are other states doing with generative AI and what is the role of the state CIO?”
National Association of State Chief Information Officers (NASCIO)
This article explores how legal documents can be treated like software programs, using methods like software testing and mutation analysis to enhance AI-driven statutory analysis, aiding legal decision-making and error detection.
This handbook provides local governments with practical guidelines, best practices, and ethical considerations for adopting and using AI tools, emphasizing transparency, human oversight, and risk management.
This session from FormFest 2024 walked attendees through some of the major changes AI is bringing to form design. Learn about the National Head Start Association’s use of AI to reduce administrative burden and the Canadian Digital Service’s tips for protecting government applications systems from AI.
The article discusses the phenomenon of model multiplicity in machine learning, arguing that developers should be legally obligated to search for less discriminatory algorithms (LDAs) to reduce disparities in algorithmic decision-making.
