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
  • [41] Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia
    Sharmat, Prateek
    Hassan, Cesare
    GASTROENTEROLOGY, 2022, 162 (04) : 1056 - 1066
  • [42] Deep Learning-Based Artificial Intelligence for Mammography
    Yoon, Jung Hyun
    Kim, Eun Kyung
    KOREAN JOURNAL OF RADIOLOGY, 2021, 22 (08) : 1225 - 1239
  • [43] Artificial intelligence and deep learning: Wittgenstein beats Plato
    Luscher, Thomas F.
    Wenzl, Florian A.
    EUROPEAN HEART JOURNAL, 2023, 44 (42) : 4403 - 4405
  • [44] Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery
    Bozzo, Anthony
    Tsui, James M. G.
    Bhatnagar, Sahir
    Forsberg, Jonathan
    JOURNAL OF THE AMERICAN ACADEMY OF ORTHOPAEDIC SURGEONS, 2024, 32 (11) : e523 - e532
  • [45] Artificial intelligence in deep learning algorithms for multimedia analysis
    Jeon, Gwanggil
    Anisetti, Marco
    Damiani, Ernesto
    Kantarci, Burak
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (45-46) : 34129 - 34139
  • [46] Artificial intelligence with deep learning in nuclear medicine and radiology
    Milan Decuyper
    Jens Maebe
    Roel Van Holen
    Stefaan Vandenberghe
    EJNMMI Physics, 8
  • [47] Artificial intelligence in deep learning algorithms for multimedia analysis
    Gwanggil Jeon
    Marco Anisetti
    Ernesto Damiani
    Burak Kantarci
    Multimedia Tools and Applications, 2020, 79 : 34129 - 34139
  • [48] Artificial Intelligence and Deep Learning Applications for Automotive Manufacturing
    Luckow, Andre
    Kennedy, Ken
    Ziolkowski, Marcin
    Djerekarov, Emil
    Cook, Matthew
    Duffy, Edward
    Schleiss, Michael
    Vorster, Bennie
    Weill, Edwin
    Kulshrestha, Ankit
    Smith, Melissa C.
    2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2018, : 3144 - 3152
  • [49] Artificial intelligence to deep learning: machine intelligence approach for drug discovery
    Gupta, Rohan
    Srivastava, Devesh
    Sahu, Mehar
    Tiwari, Swati
    Ambasta, Rashmi K.
    Kumar, Pravir
    MOLECULAR DIVERSITY, 2021, 25 (03) : 1315 - 1360
  • [50] Artificial intelligence to deep learning: machine intelligence approach for drug discovery
    Rohan Gupta
    Devesh Srivastava
    Mehar Sahu
    Swati Tiwari
    Rashmi K. Ambasta
    Pravir Kumar
    Molecular Diversity, 2021, 25 : 1315 - 1360