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Co-creating Computer Supported Collective Intelligence
in Citizen Science Hubs
Aelita Skarzauskiene1 [0000-0003-1606-0676]
Monika Mačiulienė2 [0000-0002-8527-7468]
1 Vilnius Gediminas Technical University, Vilnius, Lithuania
2 Vilnius Gediminas Technical University, Vilnius, Lithuania
aelita.skarzauskiene@vilniustech.lt
Abstract. Collective Intelligence system can be conceptualized as knowledge
network created by web-mediated interaction amongst individuals with personal
knowledge. Citizen Science Hubs are ideal environment for collective intelli-
gence to emerge and can be considered as Collective Intelligence systems. The
current research aims to deepen and expand knowledge for designing scientific
evidence supported engagement motivation strategies and developing digital sup-
ported co-creation methods. Citizen Science aims to bring different stakeholders
together and bridge society with science in an institutionalized way by develop-
ing Collective Intelligence ecosystem which entails collaboration between all QH
stakeholders: the public and researchers/institutes, also governments and funding
agencies. The development of crowdsourcing platforms and networks that enable
volunteers to contribute to different research projects, the use of machine learning
technologies and artificial intelligence may extend ecosystem capabilities, espe-
cially those depending on human intelligence. It is essential that citizen science
platforms leverage the complementary strengths of humans and machines to take
full advantage of the onslaught of data being experienced across the disciplines.
The paper presents conceptual model of Collective intelligence ecosystem focus-
ing on human-computer interaction providing insights to support Citizen Science
communities to deliver intended intellectual outcomes.
Keywords: Collective Intelligence, Co-creation, Citizen Science
1 ICT supported Collective Intelligence Systems
All the types of human groups can be regarded as a source of collective intelligence.
Community, according Luo et al. [1] “refers to any human group in which the members
have some common characteristics, share same interests or views, have similar pur-
poses.” Lykourentzou et al. [2] define online community as a “system which hosts an
adequately large group of people, who act for their individual goals, but whose group
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actions aim and may result – through technology facilitation – in a higher-level intelli-
gence and benefit of the community.” There is no doubt that the widespread and avail-
ability of the Internet is one of the prerequisites for a new form of interconnection,
different forms of social cohesion and conditions to collectively build community in-
teractions. Enabled by the information communication technologies (ICT) and under
the right circumstances “the communities may exhibit higher intelligent features than a
traditional community does because artificial intelligence provides an effective com-
munication channel for massive exchange of data, information and knowledge” [3].
The computation capabilities of the modern ICT also may be of great help for the in-
formation processing tasks within the entire community. Similar as in the case of
“swarm intelligence” [4] in natural systems, Collective Intelligence systems consist of
human beings and supporting computer systems. Human intelligence blended together
with intelligent machines enable communities to resolve problems and achieve unprec-
edented results. The Structural Model of Community Intelligence [1] explains how the
community level intelligence may generate from the knowledge-related activities of the
participants or the community members. Firstly, the community should “contain a
memory system that stores information and knowledge, analogous to the memory sys-
tem in a human brain. Secondly, the community should have the capability of ‘intelli-
gent’ problem-solving, i.e. the capability of utilizing the stored knowledge to solve
problems. Theoretical insights and empirical research results [5] reveal that at the cur-
rent knowledge level technological preconditions are important features of the CI sys-
tems and evaluating them could be useful in predicting the performance of the system
as a whole.
2 Citizen Science Hub as Collective Intelligence ecosystem
Collective Intelligence system can be conceptualized as knowledge network created by
web-mediated interaction amongst individuals with personal knowledge. Citizen Sci-
ence Hubs are ideal environment for collective intelligence to emerge and can be con-
sidered as CI systems. Citizen Science (CS) projects involve members of the general
public as active participants in research. Essentially, Citizen Science aims to bring dif-
ferent stakeholders together and bridge society with science in an institutionalized way
by developing Collective Intelligence ecosystem which entails collaboration between
all QH stakeholders: the public and researchers/institutes, also governments and fund-
ing agencies [6]. New knowledge, new ideas, found solutions, suggested problem solv-
ing methods, shaped up public opinion, structured opinions and views, developed in-
novations, prototypes, generated added value, etc. are considered to be intellectual ca-
pacities of the ecosystem. The development of crowdsourcing platforms and networks
that enable volunteers to contribute to different research projects, the use of machine
learning technologies and artificial intelligence may extend ecosystem capabilities, es-
pecially those depending on human intelligence. The knowledge network embodies the
collective knowledge of the community and consist of a technological network or me-
dia network that supports information and knowledge transfer, a human network of
community members, and a content network of knowledge and information, which is
3
hosted in humans and computer systems [1]. Human intelligence in convergence with
“machine” intelligence create opportunities for network participants to achieve impres-
sive activity results. A range of digital infrastructures (e.g. mobile apps, low-cost sen-
sors, games, and gamification) have been developed to facilitate interaction and com-
munication between citizens and scientist and to expand the scale and scope of project
and protocol design, data collection, information delivery, data processing, and visual-
ization [7,8,9]. It is essential that citizen science platforms leverage the complementary
strengths of humans and machines to take full advantage of the onslaught of data being
experienced across the disciplines [10].
With the aim to deeper understand complexity of relationships at different levels of
human-computer interaction in Collective Intelligence ecosystem, conceptual model
was developed on the basis of theoretical insights. The framework presented in Figure
1 below provides a holistic view into the Citizen Science Hub as co-creative collective
intelligence ecosystems.
Actor Content Process
Macro level
Meso level
Micro level
Public value
propositions
Economic, Social,
Political, Quality of
Life, Strategic,
Ideological,
Stewardship
QH stakeholders Value propositions created
by CS Hub ICT, Enablers & Barriers
Network value
propositions
Actor value
propositions
Resource integration processes
Service exchange processes
Networking processes
BARRIERS
Design of value propositions
Government
NGOs
Businesses
Citizens
Enablers
Partners
Initiators
Users
Wisdom of Crowds
Digital enabled
diversity
Digital enabled
self-organization,
decentralization,
independence
Human-computer interaction
Fig. 1. Citizen Science Hub as a Collective Intelligence Ecosystem
In the context of this research project, Collective Intelligence ecosystem refers to a
system in which actors work together to achieve a mutual benefit – public value. The
4
proposed model has three dimensions – actors, content and processes distributed on
three levels – Micro, Meso and Macro between economic and social actors within the
networks. Hence, the services offered by the Citizen Science Hubs are only inputs in to
public value creating activities in the context of civic society. The Micro level refers to
the direct service-for-service exchange, i.e., end-users of the platforms. The Meso level
refers to the indirect service-for-service exchange with the external stakeholders i.e.
partners or competitors. The Macro level refers to the complex relationships between
different systems with diverse interests co-creating public value. No one stakeholder
has all the resources needed to reach their goals, and each actor is a potential source of
resources for other actors within the ecosystem. Interactions happen through the digital
enhanced creation, sharing, obtainment, and integration of the resources. Collective in-
telligence emerges in the ecosystem when a number of entities work collectively to
create mutual benefits by granting access to one another’s resources including people,
technologies, organizations and information.
3 Conclusion and insights for future research
The long term vision of CI systems is “to fuse the knowledge, experience and exper-
tise residing in the minds of individuals, in order to elevate, through machine facilita-
tion, the optimal information and decisions that will lead to the benefit of the whole
community” [11]. As systems become more complex and include more connections
between humans and machines, the characteristics of those systems become important
in determining the performance and successful development of the collaborations in the
system. The challenging task for the researchers is to correlate different factors and to
find realizable possibilities for the system performance in these causal relationships.
Collective Intelligence development field requires deeper research from academic and
practical point of view. It would be important not only to identify the assumptions af-
fecting development of ecosystem, but also to predict possible evolution scenarios and
to define risk areas. Scientific viewpoint and analysis of the influence of social tech-
nologies on formation of Collective Intelligence raises many questions. Citizen science
platforms and other networks face practical problems pertaining to the existence of a
wide variety of technological tools and solutions; however, these pre-conditions do not
encourage growth of Collective Intelligence since people do not collaborate, they ex-
press their opinion but do not structure it, do not assume obligations to implement de-
cisions, etc.
From the scientific perspective, it is not the analysis of the phenomenon of Collective
Intelligence in itself that is important. Future research should focus on the identification
of the pre-conditions for the formation of collective intelligence, formulation of holistic
conceptions and collection of empirical data. Deeper understanding of Collective Intel-
ligence ecosystem is necessary to support Citizen Science communities to deliver in-
tended intellectual outcomes and to provide a possibility for practitioners to integrate
or create new tools and IT based solutions oriented towards societal social values.
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Funding: This research was funded by the European Union’s Horizon 2020 research
and innovation program under Grant Agreement No. 101005330 (INCENTIVE).
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