The AI Insights publication series from the UK Government’s Government Digital Service (GDS) provides technical guidance to help public sector organisations understand, implement, and manage artificial intelligence systems safely and effectively.
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 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.
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.
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 White House Office of Science and Technology Policy has identified five principles that should guide the design, use, and deployment of automated systems to protect the American public in the age of artificial intelligence. These principles help provide guidance whenever automated systems can meaningfully impact the public’s rights, opportunities, or access to critical needs.
This strategy document establishes a governance framework and roadmap to ensure responsible, trustworthy, and effective AI use across Canadian federal institutions.
The AI RMF Playbook offers organizations detailed, voluntary guidance for implementing the NIST AI Risk Management Framework to map, measure, manage, and govern AI risks effectively.
National Institute of Standards and Technology (NIST)
This report explores the role that academic and corporate Research Ethics Committees play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.
