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 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.
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.
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.
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.
This article examines how Chile’s SUSESO is balancing cost-focused procurement criteria with ethical AI concerns in its medical claims automation process.
This action plan outlines Oregon’s strategic approach to adopting AI in state government, emphasizing ethical use, privacy, transparency, and workforce readiness.
This strategy document establishes a governance framework and roadmap to ensure responsible, trustworthy, and effective AI use across Canadian federal institutions.
NIST has created a voluntary AI risk management framework, in partnership with public and private sectors, to promote trustworthy AI development and usage.
National Institute of Standards and Technology (NIST)
