A comprehensive assessment that maps how artificial intelligence is currently being used, governed, and managed across local, state, and federal governments in the United States.
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)
The team developed an application to simplify Medicaid and CHIP applications through LLM APIs while addressing limitations such as hallucinations and outdated information by implementing a selective input process for clean and current data.
This guide, directed at poverty lawyers, explains automated decision-making systems so lawyers and advocates can better identify the source of their clients' problems and advocate on their behalf. Relevant for practitioners, this report covers key questions around automated decision-making systems.
This paper introduces the problem of semi-automatically building decision models from eligibility policies for social services, and presents an initial emerging approach to shorten the route from policy documents to executable, interpretable and standardised decision models using AI, NLP and Knowledge Graphs. There is enormous potential of AI to assist government agencies and policy experts in scaling the production of both human-readable and machine executable policy rules, while improving transparency, interpretability, traceability and accountability of the decision making.
The state of Indiana developed a policy framework for the ethical and efficient use of artificial intelligence (AI) within state agencies. The policy adopts the National Institute of Standards and Technology’s AI Risk Management Framework to manage potential risks effectively. It also details the applicability of the actions undertaken by the Office of the Chief Data Officer (OCDO) to enable the deployment of trustworthy AI systems.
This primer is written for a non-technical audience to increase understanding of the terminology, applications, and difficulties of evaluating facial recognition technologies.
A catalogue to help teams design trustworthy services that work for people. Categories including informing decisions, signing into services, giving and removing consent, and doing security checks.
This guidebook offers an introduction to the risks of discrimination when using automated decision-making systems. This report also includes helpful definitions related to automation.
This report offers a critical framework for designing algorithmic impact assessments (AIAs) by drawing lessons from existing impact assessments in areas like environment, privacy, and human rights to ensure accountability and reduce algorithmic harms.
