Artificial intelligence for collective intelligence: a national-scale research strategy

Seth Bullock; Nirav Ajmeri; Mike Batty; Michaela Black; John Cartlidge; Robert Challen; Cangxiong Chen; Jing Chen; Joan Condell; Leon Danon; Adam Dennett; Alison Heppenstall; Paul Marshall; Phil Morgan; Aisling O'Kane; Laura G. E. Smith; Theresa Smith; Hywel T. P. Williams

doi:10.1017/S0269888924000110

Artificial intelligence for collective intelligence: a national-scale research strategy

Seth Bullock^*
, Nirav Ajmeri
, Mike Batty
, Michaela Black
, John Cartlidge
, Robert Challen
, Cangxiong Chen
, Jing Chen
, Joan Condell
, Leon Danon
, Adam Dennett
, Alison Heppenstall
, Paul Marshall
, Phil Morgan
, Aisling O'Kane
, Laura G. E. Smith
, Theresa Smith
, Hywel T. P. Williams

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

11 Citations (Scopus)

Abstract

Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

Original language	English
Article number	e10
Number of pages	23
Journal	The Knowledge Engineering Review
Volume	39
DOIs	https://doi.org/10.1017/S0269888924000110
Publication status	Published - 2 Dec 2024

Bibliographical note

Publisher Copyright:
© The Author(s), 2024.

Research Groups and Themes

Intelligent Systems Laboratory

Keywords

cs.AI
cs.CY

Access to Document

10.1017/S0269888924000110Licence: CC BY

Handle.net

Persistent link

8459 AI For Collective Intelligence
Bullock, S. (Principal Investigator)
1/02/24 → 31/01/29
Project: Research, Parent

Cite this

Bullock, S., Ajmeri, N., Batty, M., Black, M., Cartlidge, J., Challen, R., Chen, C., Chen, J., Condell, J., Danon, L., Dennett, A., Heppenstall, A., Marshall, P., Morgan, P., O'Kane, A., Smith, L. G. E., Smith, T., & Williams, H. T. P. (2024). Artificial intelligence for collective intelligence: a national-scale research strategy. The Knowledge Engineering Review, 39, Article e10. https://doi.org/10.1017/S0269888924000110

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abstract = "Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined. ",

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Bullock, S , Ajmeri, N, Batty, M, Black, M, Cartlidge, J , Challen, R, Chen, C, Chen, J, Condell, J, Danon, L, Dennett, A, Heppenstall, A, Marshall, P, Morgan, P, O'Kane, A, Smith, LGE, Smith, T & Williams, HTP 2024, 'Artificial intelligence for collective intelligence: a national-scale research strategy', The Knowledge Engineering Review, vol. 39, e10. https://doi.org/10.1017/S0269888924000110

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AU - Bullock, Seth

AU - Ajmeri, Nirav

AU - Batty, Mike

AU - Black, Michaela

AU - Cartlidge, John

AU - Challen, Robert

AU - Chen, Cangxiong

AU - Chen, Jing

AU - Condell, Joan

AU - Danon, Leon

AU - Dennett, Adam

AU - Heppenstall, Alison

AU - Marshall, Paul

AU - Morgan, Phil

AU - O'Kane, Aisling

AU - Smith, Laura G. E.

AU - Smith, Theresa

AU - Williams, Hywel T. P.

PY - 2024/12/2

Y1 - 2024/12/2

N2 - Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

AB - Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or transnational scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.

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KW - cs.CY

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DO - 10.1017/S0269888924000110

M3 - Article (Academic Journal)

SN - 0269-8889

VL - 39

JO - The Knowledge Engineering Review

JF - The Knowledge Engineering Review

M1 - e10

ER -

Artificial intelligence for collective intelligence: a national-scale research strategy

Abstract

Bibliographical note

Research Groups and Themes

Keywords

Access to Document

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Projects

8459 AI For Collective Intelligence

Cite this