This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.
This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
This UN report warns against the risks of digital welfare systems, emphasizing their potential to undermine human rights through increased surveillance, automation, and privatization of public services.
This project maps the challenges of constructing algorithmic impact assessments (AIAs) by analyzing impact assessments in other domains—from the environment to human rights to privacy and identifies ten needed components for a robust impact assessment.
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 academic article develops a framework for evaluating whether and how automated decision-making welfare systems introduce new harms and burdens for claimants, focusing on an example case from Germany.
This report presents evidence on the use of algorithmic accountability policies in different contexts from the perspective of those implementing these tools, and explores the limits of legal and policy mechanisms in ensuring safe and accountable algorithmic systems.
On December 5, 2022, an expert panel, including representatives from the White House, unpacked what’s included in the AI Bill of Rights, and explored how to operationalize such guidance among consumers, developers, and other users designing and implementing automated decisions.
This playbook provides federal agencies with guidance on implementing AI in a way that is ethical, transparent, and aligned with public trust principles.
U.S. Department of Health and Human Services (HHS)
This report explores technologies that have the potential to significantly affect employment and job quality in the public sector, the factors that drive choices about which technologies are adopted and how they are implemented, how technology will change the experience of public sector work, and what kinds of interventions can protect against potential downsides of technology use in the public sector. The report categories technologies into five overlapping categories including manual task automation, process automation, automated decision-making systems, integrated data systems, and electronic monitoring.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
