Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence

被引:27
|
作者
Buhmann, Alexander [1 ,2 ]
Fieseler, Christian [3 ,4 ]
机构
[1] BI Norwegian Business Sch, Corp Commun, Oslo, Norway
[2] Univ Fribourg, Commun Studies, Fribourg, Switzerland
[3] BI Norwegian Business Sch, Commun Management, Oslo, Norway
[4] Univ St Gallen, St Gallen, Switzerland
关键词
artificial intelligence (AI); AI ethics; AI governance; responsible innovation; political corporate social responsibility (PCSR); deliberative democracy; SUSTAINABLE DEVELOPMENT; POLITICAL ACTIVITY; DECISION-MAKING; CRITIQUE; PARTICIPATION; ORGANIZATION; CORPORATION; PERSPECTIVE; LEGITIMACY; BUSINESS;
D O I
10.1017/beq.2021.42
中图分类号
F [经济];
学科分类号
02 ;
摘要
Responsible innovation in artificial intelligence (AI) calls for public deliberation: well-informed "deep democratic" debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge of actors from the AI industry within a deliberative system. We develop a new framework of responsibilities for AI innovation as well as a deliberative governance approach for enacting these responsibilities. In elucidating this approach, we show how actors from the AI industry can most effectively engage with experts and nonexperts in different social venues to facilitate well-informed judgments on opaque AI systems and thus effectuate their democratic governance.
引用
收藏
页码:146 / 179
页数:34
相关论文
共 50 条
  • [31] Deep learning: Integrating artificial intelligence into the sonography curriculum
    Stoodley, Paul
    Oomens, Donna
    Robinson, Catherine
    SONOGRAPHY, 2024, 11 (03) : 198 - 200
  • [32] Applications of deep learning method of artificial intelligence in education
    Zhang, Fan
    Wang, Xiangyu
    Zhang, Xinhong
    EDUCATION AND INFORMATION TECHNOLOGIES, 2025, 30 (02) : 1563 - 1587
  • [33] Clinical Implications and Challenges of Artificial Intelligence and Deep Learning
    Stead, William W.
    JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2018, 320 (11): : 1107 - 1108
  • [34] Deep learning models and the limits of explainable artificial intelligence
    Jens Christian Bjerring
    Jakob Mainz
    Lauritz Munch
    Asian Journal of Philosophy, 4 (1):
  • [35] Artificial intelligence for skin permeability prediction: deep learning
    Ita, Kevin
    Roshanaei, Sahba
    JOURNAL OF DRUG TARGETING, 2024, 32 (03) : 334 - 346
  • [36] Artificial Emotional Intelligence: Conventional and deep learning approach
    Kumar, Himanshu
    Martin, A.
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 212
  • [37] Deep learning and artificial intelligence in dental diagnostic imaging
    Katsumata, Akitoshi
    JAPANESE DENTAL SCIENCE REVIEW, 2023, 59 : 329 - 333
  • [38] Artificial intelligence with deep learning in nuclear medicine and radiology
    Decuyper, Milan
    Maebe, Jens
    Van Holen, Roel
    Vandenberghe, Stefaan
    EJNMMI PHYSICS, 2021, 8 (01)
  • [39] Artificial Intelligence and Deep Learning of Head and Neck Cancer
    Razek, Ahmed Abdel Khalek Abdel
    Khaled, Reem
    Helmy, Eman
    Naglah, Ahmed
    AbdelKhalek, Amro
    El-Baz, Ayman
    MAGNETIC RESONANCE IMAGING CLINICS OF NORTH AMERICA, 2022, 30 (01) : 81 - 94
  • [40] On Artificial Intelligence and Deep Learning Within Medical Education
    Carin, Lawrence
    ACADEMIC MEDICINE, 2020, 95 (11) : S10 - S11