This Guide to Artificial Intelligence provides a strategic framework for the ethical and responsible implementation of GenA technologies in state operations.
Colorado Governor's Office of Information Technology (OIT)
This case study details the development of a document extraction prototype to streamline benefits application processing through automated data capture and classification.
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
The state of South Dakota Bureau of Information and Telecommunications (BIT) designed guidelines for the responsible use of AI-generated content in state government agencies, emphasizing the need for proofing, editing, fact-checking, and using AI-generated content as a starting point, not the finished product.
South Dakota Bureau of Information and Telecommunications
This report on the use of Generative AI in State government presents an initial analysis of the potential benefits to individuals, communities, government and State government workers, while also exploring potential risks.
This framework outlines USDA’s principles and approach to support States, localities, Tribes, and territories in responsibly using AI in the implementation and administration of USDA’s nutrition benefits and services. This framework is in response to Section 7.2(b)(ii) of Executive Order 14110 on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.
This study examines public attitudes toward balancing equity and efficiency in algorithmic resource allocation, using online advertising for SNAP enrollment as a case study.
The primer–originally prepared for the Progressive Congressional Caucus’ Tech Algorithm Briefing–explores the trade-offs and debates about algorithms and accountability across several key ethical dimensions, including fairness and bias; opacity and transparency; and lack of standards for auditing.
