In this policy brief and video, Michele Gilman summarizes evidence-based recommendations for better structuring public participation processes for AI, and underscores the urgency of enacting them.
This analysis examines the surge in U.S. state-level AI legislation in 2023, highlighting enacted laws, proposed bills, and emerging regulatory trends.
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 plan promotes responsible AI use in public benefits administration by state, local, tribal, and territorial governments, aiming to enhance program effectiveness and efficiency while meeting recipient needs.
U.S. Department of Health and Human Services (HHS)
The Center for Democracy and Technology's brief clarifies misconceptions about artificial intelligence (AI) in government services, emphasizing the need for precise definitions, awareness of AI's limitations, recognition of inherent biases, and acknowledgment of the significant resources required for effective implementation.
This post introduces EPIC's exploration of actionable recommendations and points of agreement from leading A.I. frameworks, beginning with the National Institute of Standards and Technology's AI Risk Management Framework.
This report analyzes lawsuits that have been filed within the past 10 years arising from the use of algorithm-driven systems to assess people’s eligibility for, or the distribution of, public benefits. It identifies key insights from the various cases into what went wrong and analyzes the legal arguments that plaintiffs have used to challenge those systems in court.
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.
