TY - JOUR
T1 - How large language models can reshape collective intelligence
AU - Burton, Jason W.
AU - Lopez-Lopez, Ezequiel
AU - Hechtlinger, Shahar
AU - Rahwan, Zoe
AU - Aeschbach, Samuel
AU - Bakker, Michiel A.
AU - Becker, Joshua A.
AU - Berditchevskaia, Aleks
AU - Berger, Julian
AU - Brinkmann, Levin
AU - Flek, Lucie
AU - Herzog, Stefan M.
AU - Huang, Saffron
AU - Kapoor, Sayash
AU - Narayanan, Arvind
AU - Nussberger, Anne Marie
AU - Yasseri, Taha
AU - Nickl, Pietro
AU - Almaatouq, Abdullah
AU - Hahn, Ulrike
AU - Kurvers, Ralf H.J.M.
AU - Leavy, Susan
AU - Rahwan, Iyad
AU - Siddarth, Divya
AU - Siu, Alice
AU - Woolley, Anita W.
AU - Wulff, Dirk U.
AU - Hertwig, Ralph
N1 - Publisher Copyright:
© Springer Nature Limited 2024.
PY - 2024/9
Y1 - 2024/9
N2 - Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans’ ability to collectively tackle complex problems.
AB - Collective intelligence underpins the success of groups, organizations, markets and societies. Through distributed cognition and coordination, collectives can achieve outcomes that exceed the capabilities of individuals—even experts—resulting in improved accuracy and novel capabilities. Often, collective intelligence is supported by information technology, such as online prediction markets that elicit the ‘wisdom of crowds’, online forums that structure collective deliberation or digital platforms that crowdsource knowledge from the public. Large language models, however, are transforming how information is aggregated, accessed and transmitted online. Here we focus on the unique opportunities and challenges this transformation poses for collective intelligence. We bring together interdisciplinary perspectives from industry and academia to identify potential benefits, risks, policy-relevant considerations and open research questions, culminating in a call for a closer examination of how large language models affect humans’ ability to collectively tackle complex problems.
UR - http://www.scopus.com/inward/record.url?scp=85204557088&partnerID=8YFLogxK
U2 - 10.1038/s41562-024-01959-9
DO - 10.1038/s41562-024-01959-9
M3 - Article
C2 - 39304760
AN - SCOPUS:85204557088
SN - 2397-3374
VL - 8
SP - 1643
EP - 1655
JO - Nature Human Behaviour
JF - Nature Human Behaviour
IS - 9
ER -