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Collective action on artificial intelligence: A primer and review

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  • de Neufville, Robert
  • Baum, Seth D.

Abstract

Progress on artificial intelligence (AI) requires collective action: the actions of two or more individuals or agents that in some way combine to achieve a result. Collective action is needed to increase the capabilities of AI systems and to make their impacts safer and more beneficial for the world. In recent years, a sizable but disparate literature has taken interest in AI collective action, though this literature is generally poorly grounded in the broader social science study of collective action. This paper presents a primer on fundamental concepts of collective action as they pertain to AI and a review of the AI collective action literature. The paper emphasizes (a) different types of collective action situations, such as when acting in the collective interest is or is not in individuals’ self-interest, (b) AI race scenarios, including near-term corporate and military competition and long-term races to develop advanced AI, and (c) solutions to collective action problems, including government regulations, private markets, and community self-organizing. The paper serves to bring an interdisciplinary readership up to speed on the important topic of AI collective action.

Suggested Citation

  • de Neufville, Robert & Baum, Seth D., 2021. "Collective action on artificial intelligence: A primer and review," Technology in Society, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:teinso:v:66:y:2021:i:c:s0160791x2100124x
    DOI: 10.1016/j.techsoc.2021.101649
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    References listed on IDEAS

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    Cited by:

    1. Tuba Bircan & Almila Alkim Akdag Salah, 2022. "A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    2. Han, The Anh & Lenaerts, Tom & Santos, Francisco C. & Pereira, Luís Moniz, 2022. "Voluntary safety commitments provide an escape from over-regulation in AI development," Technology in Society, Elsevier, vol. 68(C).
    3. José Javier Galán & Ramón Alberto Carrasco & Antonio LaTorre, 2022. "Military Applications of Machine Learning: A Bibliometric Perspective," Mathematics, MDPI, vol. 10(9), pages 1-27, April.
    4. Kumar, Shashank & Raut, Rakesh D. & Queiroz, Maciel M. & Narkhede, Balkrishna E., 2021. "Mapping the barriers of AI implementations in the public distribution system: The Indian experience," Technology in Society, Elsevier, vol. 67(C).
    5. Kannelønning, Mari S., 2024. "Navigating uncertainties of introducing artificial intelligence (AI) in healthcare: The role of a Norwegian network of professionals," Technology in Society, Elsevier, vol. 76(C).
    6. Hu, Mengyu & Wang, Jingyi, 2021. "Artificial intelligence in dance education: Dance for students with special educational needs," Technology in Society, Elsevier, vol. 67(C).
    7. Lavazza, Andrea & Farina, Mirko, 2023. "Leveraging autonomous weapon systems: realism and humanitarianism in modern warfare," Technology in Society, Elsevier, vol. 74(C).

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