What influences attitudes about artificial intelligence adoption: Evidence from US local officials

被引:0
|
作者
Horowitz, Michael C. [1 ]
Kahn, Lauren [2 ]
机构
[1] Univ Penn, Perry World House, Philadelphia, PA 19104 USA
[2] Council Foreign Relat, Washington, DC USA
来源
PLOS ONE | 2021年 / 16卷 / 10期
关键词
ACCEPTANCE; OPINION; SYSTEMS; NEWS;
D O I
10.1371/journal.pone.0257732
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Rapid advances in machine learning and related techniques have increased optimism about self-driving cars, autonomous surgery, and other uses of artificial intelligence (AI). But adoption of these technologies is not simply a matter of breakthroughs in the design and training of algorithms. Regulators around the world will have to make a litany of choices about law and policy surrounding AI. To advance knowledge of how they will make these choices, we draw on a unique survey pool-690 local officials in the United States-a representative sample of U.S. local officials. These officials will make many of the decisions about AI adoption, from government use to regulation, given the decentralized structure of the United States. The results show larger levels of support for autonomous vehicles than autonomous surgery. Moreover, those that used ridesharing apps prior to the COVID-19 pandemic are significantly more supportive of autonomous vehicles. We also find that self-reported familiarity with AI is correlated with increased approval of AI uses in a variety of areas, including facial recognition, natural disaster impact planning, and even military surveillance. Related, those who expressed greater opposition to AI adoption also appear more concerned about trade-offs between privacy and information and bias in algorithms. Finally, the explanatory logic used by respondents varies based on gender and prior experience with AI, which we demonstrate with quantitative text analysis.
引用
收藏
页数:20
相关论文
共 50 条
  • [31] What have we learned about artificial intelligence from studying the brain?
    Gershman, Samuel J.
    BIOLOGICAL CYBERNETICS, 2024, 118 (1-2) : 1 - 5
  • [32] Medical Specialists' Perception About Adoption of Artificial Intelligence in the Healthcare Sector
    Thakkar, Bhumi
    Bharathi, Vijayakumar S.
    CARDIOMETRY, 2022, (25): : 426 - 434
  • [33] Linguistic Framing of Artificial Intelligence: What Language to Use When Talking about Artificial Intelligence
    Lammers, Svenja
    Lasch, Alexander
    CHEMIE INGENIEUR TECHNIK, 2023, 95 (07) : 1012 - 1017
  • [34] How are US hospitals adopting artificial intelligence? Early evidence from 2022
    Baten, Redwan Bin Abdul
    HEALTH AFFAIRS SCHOLAR, 2024, 2 (10):
  • [35] Perceptions and attitudes of nurse practitioners toward artificial intelligence adoption in health care
    Rony, Moustaq Karim Khan
    Numan, Sharker Md.
    Johra, Fateha tuj
    Akter, Khadiza
    Akter, Fazila
    Debnath, Mitun
    Mondal, Sujit
    Wahiduzzaman, Md.
    Das, Mousumi
    Ullah, Mohammad
    Rahman, Mohammad Habibur
    Das Bala, Shuvashish
    Parvin, Mst. Rina
    HEALTH SCIENCE REPORTS, 2024, 7 (08)
  • [36] From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence
    George, Dileep
    Lazaro-Gredilla, Miguel
    Guntupalli, J. Swaroop
    FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2020, 14
  • [37] What you tell us about Artificial Organs
    Labbate, Alison
    ARTIFICIAL ORGANS, 2007, 31 (07) : 498 - 499
  • [38] Parent and sibling influences on adolescent alcohol use and misuse: Evidence from a US adoption cohort
    McGue, M
    Sharma, A
    Benson, P
    JOURNAL OF STUDIES ON ALCOHOL, 1996, 57 (01): : 8 - 18
  • [39] What to Expect From Artificial Intelligence
    Agrawal, Ajay
    Gans, Joshua S.
    Goldfarb, Avi
    MIT SLOAN MANAGEMENT REVIEW, 2017, 58 (03) : 23 - 26
  • [40] Does artificial intelligence suppress firms' greenwashing behavior? Evidence from robot adoption in China
    Bai, Caiquan
    Yao, Di
    Xue, Qihang
    ENERGY ECONOMICS, 2025, 142