This framework provides practical guidance, detailed reference designs, and example solutions to help organizations securely adopt and operationalize Zero Trust principles across diverse IT environments.
National Institute of Standards and Technology (NIST)
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
This report explores how despite unresolved concerns, an audit-centered algorithmic accountability approach is being rapidly mainstreamed into voluntary frameworks and regulations.
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.
This hub introduces the UK government's Algorithmic Transparency Recording Standard (ATRS), a structured framework for public sector bodies to disclose how they use algorithmic tools in decision-making.
This report provides an overview of the task force’s work in assessing, guiding, and recommending policies for the safe, ethical, and effective use of generative AI across Alabama’s executive-branch agencies.
State of Alabama Generative Artificial Intelligence (GenAI) Task Force
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 review evaluates the UK public sector's use of digital technology, identifying successes and systemic challenges, and proposes reforms to enhance service delivery.
