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 article examines how Chile’s SUSESO is balancing cost-focused procurement criteria with ethical AI concerns in its medical claims automation process.
This report offers a detailed assessment of how AI and emerging technologies could impact the Social Security Administration’s disability benefits determinations, recommending guardrails and principles to protect applicant rights, mitigate bias, and promote fairness.
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 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.
The report examines how AI deployment across state and local public administration such as chatbots, voice transcription, content summarization, and eligibility automation are reshaping government work.
This report analyzes the growing use of generative AI, particularly large language models, in enabling and scaling fraudulent activities, exploring the evolving tactics, risks, and potential countermeasures.
This report examines how governments use AI systems to allocate public resources and provides recommendations to ensure these tools promote equity, transparency, and fairness.
An academic research paper introducing SHADES, a multilingual benchmark designed to evaluate how large language models (LLMs) generate and reinforce stereotypes across different languages and cultural contexts.
Sarah Bargal provides an overview of AI, machine learning, and deep learning, illustrating their potential for both positive and negative applications, including authentication, adversarial attacks, deepfakes, generative models, personalization, and ethical concerns.
