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)
A tool for CDOs advocating for funding, authority, and expansion—and a primer for government leaders unfamiliar with the role. The report establishes shared vocabulary, identifies six CDO office archetypes, and offers cross-state insights on structures, priorities, and challenges.
This report analyzes the critical role of SNAP’s "broad-based categorical eligibility" (BBCE) policy and the widespread consequences of its potential elimination by the Trump Administration.
This article explores how AI and Rules as Code are turning law into automated systems, including how governance focused on transparency, explainability, and risk management can ensure these digital legal frameworks stay reliable and fair.
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.
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.
The Digital Identity Community of Practice kick-off event featured key resources, a new research publication on account creation and identity proofing, and insights from multiple speakers.
This paper examines the challenges U.S. state and local digital service teams face in retaining talent and offers strategies to improve retention and team stability.
This report shares the progress of the Biden-Harris Administration on health care access, prescription drug affordability, mental health, maternal health, and public health investments.
