Regulating the machine: An exploratory study of US state legislations addressing Artificial Intelligence, 2019-2023

政府(语言学) 政治学 转化式学习 国家(计算机科学) 人工智能 私营部门 大政府 人工智能应用 民主 公共行政 工程类 法律与经济学 计算机科学 法学 社会学 政治 教育学 哲学 语言学 算法
作者
Nic DePaula,Lu Gao,Sehl Mellouli,Luis F. Luna‐Reyes,Teresa M. Harrison
标识
DOI:10.1145/3657054.3657148
摘要

Artificial Intelligence (AI) poses transformative and disruptive challenges for democracy, for policy makers, and for government agencies. While various policy initiatives around the world seek to regulate AI, in the United States (US) federal government there is no sign of a comprehensive AI law, and few legal measures to enable or restrict AI have been proposed and passed. However, states across the US are active in attempting to address issues related to AI and have proposed hundreds of legislations related to AI in the past few years. In this paper, we examined what these legislations have sought to accomplish in relation to AI, and the potential impacts for the public in general and for public administration in particular. From a preliminary and descriptive analysis of all US state legislations related to AI passed from 2019 to 2023, we show how these legislations are addressing AI in terms of: (1) the types of legislations adopted or enacted; (2) the definitions of AI and associated technologies given; (3) the sectors and domains principally addressed in AI legislations; (4) the private sector and government actions directed by the legislations; and (5) how ethical and economic considerations are addressed. We generally found a lack of definition of AI, and associated technologies mentioned are rarely specific. Many of the laws create commissions or task forces to study AI, addressing the various practical and ethical issues related to AI. Legislations have created some regulations and support for industry, and have directed government agencies to identify existing AI capabilities and how AI may be employed in their agencies and jurisdictions. Considerable emphasis has been placed on issues of bias and discrimination, as well education and economic investment in AI, although unevenly distributed across states. We summarize and discuss these results in relation to existing literature and make some recommendations on how state legislatures may better address AI in the future.
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