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.
A panel of experts discuss the application of civil rights protections to emerging AI technologies, highlighting potential harms, the need for inclusive teams, and the importance of avoiding technology-centric solutions to social problems.
This action plan outlines Oregon’s strategic approach to adopting AI in state government, emphasizing ethical use, privacy, transparency, and workforce readiness.
This paper explores how legacy procurement processes in U.S. cities shape the acquisition and governance of AI tools, based on interviews with local government employees.
This is a searchable tool that compiles and categorizes over 4,700 policy recommendations submitted in response to the U.S. government's 2025 Request for Information on artificial intelligence policy.
This toolkit provides guidance to protect participant confidentiality in human services research and evaluation, including legal frameworks, risk assessment strategies, and best practices.
U.S. Department of Health and Human Services (HHS)
This strategy document establishes a governance framework and roadmap to ensure responsible, trustworthy, and effective AI use across Canadian federal institutions.
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
In early 2023, Wired magazine ran four pieces exploring the use of algorithms to identify fraud in public benefits and potential harms, deeply exploring cases from Europe.
This article examines how Chile’s SUSESO is balancing cost-focused procurement criteria with ethical AI concerns in its medical claims automation process.
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.
