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)
This policy brief explores how federal privacy laws like the Privacy Act of 1974 limit demographic data collection, undermining government efforts to conduct equity assessments and address algorithmic bias.
This UN report warns against the risks of digital welfare systems, emphasizing their potential to undermine human rights through increased surveillance, automation, and privatization of public services.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
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.
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 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 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 academic paper examines predictive optimization, a category of decision-making algorithms that use machine learning (ML) to predict future outcomes of interest about individuals. Through this examination, the authors explore how predictive optimization can raise concerns that make its use illegitimate and challenge claims about predictive optimization's accuracy, efficiency, and fairness.
This resource helps individuals with aligning their work with the needs of the communities they wish to serve, while reducing the likelihood of harms and risks those communities may face due to the development and deployment of AI technologies.
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.
