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.
An in-depth report that examines how states use automated eligibility algorithms for home and community-based services (HCBS) under Medicaid and assesses their implications for access and fairness.
A workshop led by Elham Ali on integrating the principles of human-centered design and equity to Artificial Intelligence (AI) design, use, and evaluation.
Artificial intelligence promises exciting new opportunities for the government to make policy, deliver services and engage with residents. But government procurement practices need to adapt if we are to ensure that rapidly-evolving AI tools meet intended purposes, avoid bias, and minimize risks to people, organizations, and communities. This report lays out five distinct challenges related to procuring AI in government.
This panel discussion from the Academy's 2025 Policy Summit explores the intersection of artificial intelligence (AI) and public benefits, examining how technological advancements are influencing policy decisions and the delivery of social services.
This review evaluates the UK public sector's use of digital technology, identifying successes and systemic challenges, and proposes reforms to enhance service delivery.
This is the summary version of a report that documents four experiments exploring if AI can be used to expedite the translation of SNAP and Medicaid policies into software code for implementation in public benefits eligibility and enrollment systems under a Rules as Code approach.
This framework provides voluntary guidance to help employers use AI hiring technology in ways that are inclusive of people with disabilities, while aligning with federal risk management standards.
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)
