This study examines the adoption and implementation of AI chatbots in U.S. state governments, identifying key drivers, challenges, and best practices for public sector chatbot deployment.
This resource examines how improvements in customer service experiences in public benefit programs like Medicaid, CHIP, and TANF can help better meet enrollees’ needs and build trust in government.
This report summarizes 19 SNAP policy options (in effect as of Oct. 1, 2023) and waivers (implemented as of July 1, 2023) chosen by SNAP state agencies (50 states, the District of Columbia, Guam, and Virgin Islands) in federal fiscal year (FY) 2023.
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.
Originally created for use by federal staff at the U.S. Department of Health and Human Services, this tool describes the six steps for conducting equity assessments and provides tips for completing each step.
Office of the Assistant Secretary for Planning and Evaluation (ASPE)
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.
Tech Talent Project has partnered with American Enterprise Institute, the Beeck Center for Social Impact + Innovation at Georgetown University and New America to release a series of memos to help states make quick, meaningful progress in building technical capacity while avoiding past pitfalls.
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.
What exactly are the differences between generative AI, large language models, and foundation models? This post aims to clarify what each of these three terms mean, how they overlap, and how they differ.
Center for Security and Emerging Technology (CSET)
