Will artificial intelligence replace human intelligence in citizen science projects? Should we create synergies, instead of replacing one thing with another? Kaori Otsu, CREAF researcher and part of the Cos4Cloud team, reflects on this topic in this presentation.

1. Could AI replace human
intelligence?
A systematic review on citizen science
Kaori Otsu k.otsu@creaf.uab.cat
Joan Masó
Centre for Ecological Research and Forestry Applications
(CREAF)
6 October 2022 Berlin
ECSA 2022 Oral Session 2: Apps and Sensors

2. 6/10/2022 ECSA 2022 Berlin 2
Background
With increasing citizen science activities, AI contributions have
recently accelerated to simplify data collection and management,
connected to citizen observatories and mobile applications.
Human
Intelligence
• data validation
by domain
experts or citizen
scientists
• classification,
identification
Artificial
Intelligence
• task automation
partially or fully
• data collection,
training and
processing,
object detection
(Source: McClure et al. 2020)
AI
ML
DL

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Motivation
1. Identify existing AI approaches to
automate tasks in citizen science projects
2. Analyse research trends and gaps in the
area of AI applied to citizen science data

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Methodology
Systematic literature review by the Web of Science
– Topic Search TS =
("artificial intelligence" AND
"citizen science")
– Refined by:
• Languages (English)
• Publication Years (NOT 2022)
= 89 articles

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Methodology
Parameters Examples
Domain plant, animal, marine, environment, astronomy, medical
Automated task species identification, object detection, galaxy classification, prediction,
monitoring, decision-making
AI type machine learning (k-means, k-Nearest Neighbours, Random Forest, decision tree)
deep learning (Convolutional Neural Network)
Platform
Application
Zooniverse, Pl@ntNet, iNaturalist, Siri, Alexa, BirdNet, MammalWeb, eMammal,
WildBook, eBird, BeeWatch, Braindr, GalaxyZoo, MilkyWay, FreshWater Watch,
SeagrassSpotter, Mangrove Watch, Coral Watch, Reef Check, Redmap
Project Heritage Quest, Penguin Watch, Flora Incognita, Earth Watch, Gravity Spy,
HumBug, Earthquake Detective, Mosquito Alert
Region Africa, Asia-Pacific, Europe, North America, South America, Earth, Planet/Galaxy

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Results – Publication
0
5
10
15
20
25
2013 2014 2015 2016 2017 2018 2019 2020 2021
Articles
Year

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Results – Domain
0
2
4
6
8
10
12
14
16
Agri/food
Animal
Archaeology
Astronomy
Bio/medical
Crowdsourcing
Disaster
Environment
Insect
Marine
Plant
Psychology
Articles
Domain

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Results – AI Type
0
5
10
15
20
2013 2014 2015 2016 2017 2018 2019 2020
2021
Articles
Year
DL
ML

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Results – Region
0
5
10
15
20
25
Africa Asia+Pacific Europe+UK North
America
South
America
Earth Planet /
Galaxy
Articles
Region

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Results – Trends
• First publication year in 2013 with the continuous increase
• Main domain areas in astronomy and overall biodiversity
• AI type shifts in 2017 from Machine Learning to Deep Learning
• Geographical distribution centred in Europe and North America

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Results – Gaps
• Evenness of geographical coverage and frequency
• Transparency of automated processes
– Algorithm types, source of training samples
• Accuracy of results based on AI
– Biases, quality of training datasets
• Balance of using both AI and human intelligence
– Ethical and societal resistance against AI technologies

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Discussion
• AI interaction
– The variability in accuracy among different algorithms and methods should be
explored to gain trust.
• Human interaction
– Replacing with AI may demotivate experts and citizens.
– Ethical principles should be considered for organisations and individuals.
• AI – Human interaction
– AI could refine citizen science platforms and applications in service lifecycle:
• updated features and functions to meet user needs
• user feedback, co-designed projects (e.g. Cos4Cloud).

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Conclusions
• Validation and decision-making by human experts will continue to serve,
depending on the complexity of data analysis.
– Probability thresholds calculated by AI
• Hybrid approaches of AI and human intelligence are both complementary
to increase synergy between them.
– Optimal trade-off between efficiency and accuracy in output
– Citizen science data integrated in training datasets for AI

14. Thank you!
Vielen Dank!
Co-designed citizen observatories for the EOS-Cloud