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 brief outlines the U.S. federal government’s framework to identify, reduce, and address administrative burdens through a series of executive orders, legislative actions, and updated policies focused on improving customer experience and increasing access to government benefits.
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.
This report explores key questions that a focus on disability raises for the project of understanding the social implications of AI, and for ensuring that AI technologies don’t reproduce and extend histories of marginalization.
Algorithmic impact assessments (AIAs) are an emergent form of accountability for organizations that build and deploy automated decision-support systems. This academic paper explores how to co-construct impacts that closely reflects harms, and emphasizes the need for input of various types of expertise and affected communities.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
This paper analyzes the unique challenges of conducting participatory design in large-scale public projects, focusing on stakeholder management, fostering engagement, and integrating participatory methods into institutional transformation.
This study highlights a 2.2% increase in citizen satisfaction with federal services, reaching a seven-year high, driven by improvements in digital experiences, government processes, and complaint handling.
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.
