Artificial intelligence (AI) is on the rise in all areas of life, including in citizen science (CS) where it can either help to or completely solve a problem, influence participant behavior or both. We are currently investigating the potential role and benefit of the use of hybrid intelligence (HI) in CS. In particular, we explore two new dimensions of CS: level of digitization, and amount of necessary knowledge or experience needed for participation – as typologies until now have mainly focused on participant involvement in different stages of the research process which is not directly linked to either of these new dimensions. Viewing CS through a HI lens via these two new dimensions allows the CS community to better visualize how AI/HI can support and be used in their projects in the future, and allows the AI community to gain ideas for how investigating AI in CS projects can further their own field.

Our used definitions

Artificial Intelligence (AI) is a term used to describe machines exhibiting traits usually associated with the human mind such as learning and problem solving (Russell and Norvig 2009). More formally, the scientific field of AI studies intelligent agents, that is devices that have the ability to achieve their goals in a wide range of environments (Legg and Hutter 2007).

Citizen Science (CS) can be interpreted in many different ways (Heigl and Dörler 2017; Auerbach et al. 2019) but for the purpose of our research we see it as “Scientific work undertaken by members of the general public, often in collaboration with or under the direction of professional scientists and scientific institutions” (Oxford English Dictionary (2014)) where the participants are not the main subjects of the study (Kullenberg and Kasperowski 2016; Wiggins and Crowston 2011).

Hybrid Intelligence (HI) is defined as the ability to achieve complex goals by combining human and artificial intelligence, thereby reaching superior results to those each of them could have accomplished separately, and continuously improve by learning from each other (Dellermann et al. 2019).


Center for Hybrid Intelligence Project Team

Jacob Sherson, Professor MSO

Janet Rafner, Director of Learning

Gitte Kragh, Postdoc, Citizen science

Arthur Hjorth, Postdoc, Didactics and Learning

Miroslav Gajadcz, Postdoc, Data Science

Blanka Palfi, Masters student, Cognitive Science


Aleks Berditchevskaia, Centre for Collective Intelligence Design.

François Grey, University of Geneva.

Kobi Gal, Ben-Gurion University of the Negev.

Avi Segal, Ben-Gurion University of the Negev.

Mike WalmsleyUniversity of Oxford

Dominik Dellermann, vencortex and Universität St.Gallen

Muki Haklay, University College London

Pietro Michelucci, Human Computation Institute



Auerbach, Jeremy, Erika L. Barthelmess, Darlene Cavalier, Caren B. Cooper, Heather Fenyk, Mordechai Haklay, Joseph M. Hulbert, et al. 2019. ‘The Problem with Delineating Narrow Criteria for Citizen Science’. Proceedings of the National Academy of Sciences 116 (31): 15336–37.
Dellermann, Dominik, Philipp Ebel, Matthias Söllner, and Jan Marco Leimeister. 2019. ‘Hybrid Intelligence’. Business & Information Systems Engineering 61 (5): 637–43.
Heigl, Florian, and Daniel Dörler. 2017. ‘Public Participation: Time for a Definition of Citizen Science’. Nature 551 (7679): 168–168.
Kullenberg, Christopher, and Dick Kasperowski. 2016. ‘What Is Citizen Science? – A Scientometric Meta-Analysis’. PLoS ONE 11 (1).
Legg, Shane, and Marcus Hutter. 2007. ‘A Collection of Definitions of Intelligence’. ArXiv:0706.3639 [Cs], June.
Russell, Stuart, and Peter Norvig. 2009. Artificial Intelligence: A Modern Approach. 3rd Edition. Upper Saddle River: Pearson.
Wiggins, Andrea, and Kevin Crowston. 2011. ‘From Conservation to Crowdsourcing: A Typology of Citizen Science’. In 2011 44th Hawaii International Conference on System Sciences, 1–10.