Ethics and governance of artificial intelligence: Evidence from a survey of machine learning researchers

被引:0
|
作者
Zhang B. [1 ]
Anderljung M. [2 ]
Kahn L. [3 ]
Dreksler N. [2 ]
Horowitz M.C. [3 ]
Dafoe A. [2 ]
机构
[1] Department of Government, Cornell University, Ithaca, 14853, NY
[2] Centre for the Governance of AI, Oxford
[3] Perry World House, University of Pennsylvania, Philadelphia, 19104, PA
来源
| 1600年 / AI Access Foundation卷 / 71期
关键词
Surveys;
D O I
10.1613/JAIR.1.12895
中图分类号
学科分类号
摘要
Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including through their work, advocacy, and choice of employment. Nevertheless, this influential group’s attitudes are not well understood, undermining our ability to discern consensuses or disagreements between AI/ML researchers. To examine these researchers’ views, we conducted a survey of those who published in two top AI/ML conferences (N = 524). We compare these results with those from a 2016 survey of AI/ML researchers (Grace et al., 2018) and a 2018 survey of the US public (Zhang & Dafoe, 2020). We find that AI/ML researchers place high levels of trust in international organizations and scientific organizations to shape the development and use of AI in the public interest; moderate trust in most Western tech companies; and low trust in national militaries, Chinese tech companies, and Facebook. While the respondents were overwhelmingly opposed to AI/ML researchers working on lethal autonomous weapons, they are less opposed to researchers working on other military applications of AI, particularly logistics algorithms. A strong majority of respondents think that AI safety research should be prioritized and that ML institutions should conduct pre-publication review to assess potential harms. Being closer to the technology itself, AI/ML researchers are well placed to highlight new risks and develop technical solutions, so this novel attempt to measure their attitudes has broad relevance. The findings should help to improve how researchers, private sector executives, and policymakers think about regulations, governance frameworks, guiding principles, and national and international governance strategies for AI. ©2021 AI Access Foundation.
引用
收藏
页码:591 / 666
页数:75
相关论文
共 50 条
  • [21] MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
    Pedoia, V.
    OSTEOARTHRITIS AND CARTILAGE, 2020, 28 : S16 - S16
  • [22] Artificial intelligence and machine learning
    Niklas Kühl
    Max Schemmer
    Marc Goutier
    Gerhard Satzger
    Electronic Markets, 2022, 32 : 2235 - 2244
  • [23] Machine, Identity. Ethics of Artificial Intelligence
    Vogel, Beatrix
    PHILOSOPHISCHES JAHRBUCH, 2023, 130 (02): : 149 - 151
  • [24] Artificial intelligence for parking forecasting: an extensive survey of machine learning techniques
    Cao, Rong
    Choudhury, Farhana
    Winter, Stephan
    Wang, David Z. W.
    TRANSPORTMETRICA A-TRANSPORT SCIENCE, 2024,
  • [25] Protecting artificial intelligence IPs: a survey of watermarking and fingerprinting for machine learning
    Regazzoni, Francesco
    Palmieri, Paolo
    Smailbegovic, Fethulah
    Cammarota, Rosario
    Polian, Ilia
    CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY, 2021, 6 (02) : 180 - 191
  • [26] Learning from artificial intelligence researchers about international business implications
    Ratten, Vanessa
    Hasan, Rakibul
    Kumar, Deepak
    Bustard, John
    Ojala, Arto
    Salamzadeh, Yashar
    THUNDERBIRD INTERNATIONAL BUSINESS REVIEW, 2024, 66 (02) : 211 - 219
  • [27] Applications of Artificial Intelligence and Machine Learning in the Area of SDN and NFV: A Survey
    Gebremariam, Anteneh A.
    Usman, Muhammad
    Qaraqe, Marwa
    2019 16TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD), 2019, : 545 - 549
  • [28] Evidence, ethics and the promise of artificial intelligence in psychiatry
    McCradden, Melissa
    Hui, Katrina
    Buchman, Daniel Z.
    JOURNAL OF MEDICAL ETHICS, 2023, 49 (08) : 573 - 579
  • [29] Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning
    Wang, Danshi
    Zhang, Min
    FRONTIERS IN COMMUNICATIONS AND NETWORKS, 2021, 2
  • [30] Artificial Intelligence, Machine Learning and Deep Learning
    Ongsulee, Pariwat
    2017 15TH INTERNATIONAL CONFERENCE ON ICT AND KNOWLEDGE ENGINEERING (ICT&KE), 2017, : 92 - 97